A Comparison of Alternative Approaches to Temporal Matching of Spectral Clusters in Post-Catastrophic Landscape Dynamics

Monitoring dynamic post-catastrophic landscapes necessitates unsupervised classification approaches capable of incorporating newly emerging landscape-cover states without relying on predefined classes. Within this framework, the temporal matching of independently derived spectral clusters presents a critical methodological challenge. This study compared alternative temporal matching approaches for multi-temporal Sentinel-2 imagery of the post-catastrophic floodplain landscape of Khortytsia Island (Ukraine) from 2021 to 2026. In addition to conventional methods based on centroid distance, Mahalanobis distance, Linear Discriminant Analysis, and Random Forest, geometrically oriented approaches employing the elongation and principal-axis orientation of spectral point clouds were evaluated. A series of tests assessed matching accuracy, robustness to seasonal and interannual drift, graph connectivity, and consensus structure among alternative matching solutions. The results demonstrated that geometrically oriented approaches preserved temporal correspondence among landscape-cover states with high stability despite phenological and interannual variability. In particular, axis-based matching more effectively maintained separation between corresponding and competing clusters amid progressive temporal divergence. Consensus analysis revealed that disagreement among methods was concentrated in ecotonal and actively transforming zones, indicating areas of increased landscape instability. This study shows that the geometry of spectral trajectories contains valuable information for temporal matching and provides a promising foundation for monitoring dynamic post-catastrophic landscape systems.