Sub-Pixel Mapping for Change Detection in Fluvial Environments

Satellite images with a frequent revisit period and a large area of simultaneous coverage are an ideal data source for monitoring many natural features including fluvial environments. Openly available remote sensing data have a spatial resolution that may be too coarse for accurate detection of features of interest such as gravel bars. We therefore developed a sub-pixel mapping method based on spectral mixture analysis using Sentinel-2 and Landsat images. The fraction maps were found to be more accurate than maps produced by hard classification using the same input data. We produced fraction maps of gravel, vegetation, and water presence for the Soča and Sava rivers in Slovenia, and the Vjosa river in Albania for a period of over 30 years. The thematic accuracy of the maps was within 90%. We also tested the ability of fraction maps for change detection and found that changes of at least 400 m² could be accurately detected. The developed method can be applied to study areas where less in situ data are available. More informed management decisions can be made based on newly acquired knowledge.