Monitoring riparian vegetation with up-to-date technologies, such as remote sensing and machine learning, is emphasized by their extensive and dynamic nature. This study evaluates the spatiotemporal dynamics of vegetation in the Slovak Danube riparian zone from 2004 to 2022 using a multi-sensor approach (Landsat-5/8 and Sentinel-2). We integrated seasonal vegetation indices, textural, and morphological patterns to enhance Land Cover (LC) classification accuracy. Using Random Forest within a spectral–spatial framework, we achieved notable improvement in classification accuracy. We evaluated ecological processes, spatial errors, misclassification, and the spatial distribution of LC changes within the riparian zone. Our core-area analysis revealed that only 52% of the riparian zone remains stable over time. Crucially, we quantified the impact of spatial misclassifications at class boundaries, finding that edge-related errors account for 0.24% to 2.85% of the detected change. The classification distinguished 1,076 ha of managed plantations and 1,236 ha of natural forest. Overall, the results demonstrated the effectiveness of various feature combinations for classifying different satellite datasets and assessing the temporal evolution of riparian zones in regulated river systems, while highlighting the critical importance of incorporating uncertainty analysis to ensure robust interpretation of detected LC changes.
Afzali et al. (Sun,) studied this question.
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