Intertidal coastal environments are among the most dynamic and challenging systems to monitor. They are shaped by complex interactions among tidal cycles, rainfall, groundwater fluxes and solar exposure, with these dynamics amplified in arid and semi‑arid regions by extreme conditions such as high salinity, elevated temperatures and low annual rainfall. Despite this inherent variability, intertidal habitats are still commonly assessed using static maps and single‑epoch baselines, limiting our ability to detect change, attribute drivers, and anticipate future trajectories.
As pressures from climate change, coastal development and extreme events intensify, there is an increasing need to move beyond static representations towards approaches that explicitly quantify change through time. Here, we demonstrate how Earth observation time‑series data can be used to characterise coastal dynamics and detect statistically robust trends across intertidal habitats. These approaches reveal patterns of degradation and recovery, identify impact events, and capture cyclic and seasonal variability that are often obscured or misinterpreted using traditional remote‑sensing classification and modelling approaches.
We highlight the critical role of time‑series analysis for environmental impact assessment, baseline assessment, ongoing monitoring and future prediction. Time series modelling provides critical context for natural variability that repeated static mapping may not be able to. We show that including temporal analysis into coastal monitoring frameworks may be essential for adaptive management, improved regulatory decision‑making, and more realistic representations of dynamic coastal systems.