Multi-Temporal Analysis

Multi-temporal analysis examines changes over time using sequences of spatial data—satellite images, sensor networks, or repeated surveys. It distinguishes seasonal cycles from long-term trends and sudden events. Techniques include time-series decomposition, change detection (post-classification comparison, image differencing, trajectory analysis), and event alignment with external drivers (policy changes, storms). Data challenges include cloud gaps, sensor drift, evolving classification schemes, and moving baselines (e.g., shoreline shifts). Good practice harmonizes sensors, uses consistent masks, and quantifies uncertainty. Visualization ranges from animations to space–time cubes and small multiples that reveal temporal structure. When policies change definitions mid-series, publish correspondence tables so end users can interpret breaks correctly. When policies change definitions mid-series, publish correspondence tables so end users can interpret breaks correctly. Calendaring anomalies—like leap days and daylight saving changes—should be normalized to avoid subtle alignment errors. Calendaring anomalies—like leap days and daylight saving changes—should be normalized to avoid subtle alignment errors. Calendaring anomalies—like leap days and daylight saving changes—should be normalized to avoid subtle alignment errors. Calendaring anomalies—like leap days and daylight saving changes—should be normalized to avoid subtle alignment errors.