Remote-Sensing Based Environmental Surveillance System for Detecting Early Hydrological Disruptions

Early detection of hydrological disruptions is critical for safeguarding water resources, preventing ecosystem degradation, and supporting climate-resilient environmental management. This study proposes a Remote-Sensing Based Environmental Surveillance System designed to integrate multi-sensor satellite imagery, hydrometeorological datasets, and machine learning analytics to identify subtle precursors of hydrological anomalies in vulnerable landscapes. The system leverages high-resolution optical, thermal, and radar remote-sensing data to monitor spatiotemporal variations in surface water extent, evapotranspiration rates, soil moisture dynamics, vegetation health, and land-surface temperature anomalies. By applying advanced change-detection algorithms, spectral index assessments, and time-series trend modeling, the system captures early signals of disturbances such as declining groundwater recharge, altered streamflow regimes, watershed desiccation, and potential flood-inducing surface saturation. The surveillance framework incorporates automated anomaly detection using classification models trained to differentiate between natural seasonal variability and environmentally significant deviations associated with anthropogenic pressures or climate-driven extremes. Integration of GIS-based spatial analytics enhances the interpretation of remotely sensed hydrological indicators by providing contextual layers such as watershed boundaries, land use patterns, geomorphological characteristics, and proximity to critical water infrastructure. These combined datasets allow decision-makers to pinpoint emerging hydrological stress zones and assess their cascading impacts on agriculture, biodiversity, and community water security. The system also includes a predictive module that simulates short-term hydrological trajectories under observed environmental changes, enabling proactive planning and timely deployment of mitigation measures. Application of the surveillance system to representative watersheds demonstrates its effectiveness in detecting early hydrological disruptions several weeks before they become operationally significant. Results highlight the substantial advantages of remote sensing for continuous, non-intrusive, and spatially comprehensive environmental monitoring, particularly in regions where ground-based measurements are limited or logistically challenging. By enhancing early warning capabilities, the Remote-Sensing Based Environmental Surveillance System supports climate adaptation planning, ecosystem protection, and sustainable water resource management. Ultimately, this research contributes a scalable, data-driven solution that strengthens environmental surveillance networks and improves societal resilience to hydrological instability.