Spatiotemporal Delineation and Visualization of Coastal Environmental Dynamic Zones: A Case Study of TSS Modis, RCVMAX, and Sea Level Anomaly Variability in the Barrier Island of Cigu District, Taiwan

This study addresses the need to identify and characterize dynamic zones within coastal environments by examining the spatiotemporal variability of Total Suspended Solids (TSS MODIS), land surface state dynamics derived from the Relative Change Vector Maximum (RCVMAX), and sea level anomaly (SLA) within a barrier island system. The analysis focuses on the barrier island system of Cigu District, southwestern Taiwan, using a dataset spanning from 1 January 2015 to 30 June 2025. The study area encompasses the barrier sandbar and lagoon-facing coastal environment, extending approximately from 23.10° to 23.17° N and from 120.05° to 120.10° E, including ocean-exposed shorelines, tidal inlets, and internal lagoonal environments. Dynamic zones were delineated by quantifying day-to-day absolute changes in each variable at the grid level across more than nine million grid cells. High-change thresholds were defined using the 90th percentile, based on daily variability for SLA and annual variability for TSS MODIS and RCVMAX, enabling the identification of short-term extreme dynamics. SLA exhibits a continuous spatial pattern aligned with the main axis of the barrier island, reflecting coherent hydrodynamic forcing along the ocean-facing coast that is expected to intensify under projected sea level rise scenarios. TSS MODIS variability is spatially clustered, with pronounced changes in lagoonal waters, tidal channels, and nearshore embayments, indicating localized sediment resuspension and redistribution processes that are sensitive to changes in storm frequency, wave climate, and hydrodynamic energy. RCVMAX-derived dynamic zones capture temporal surface condition transitions rather than permanent land conversion, reflecting shifts between wet and dry states and between vegetated and non-vegetated surfaces driven by tidal inundation, exposure, and vegetation phenology that may be altered by climate-driven changes in inundation regimes and coastal ecological dynamics. Quantitatively, 258,524 high-change events were identified for TSS MODIS (≥ 0.00025), 249,133 for RCVMAX (≥ 1.009), and 333,904 for SLA (≥ 0.0349 m). All dynamic zone records were archived as individual CSV datasets. By mapping areas of recurrent short-term variability, the results provide a spatially explicit foundation for anticipating barrier island responses to future climate change, supporting adaptive coastal management, nature-based solutions, and long-term planning strategies.