Refining image-based approaches for river monitoring: a focus on graphical enhancement and software parameter sensitivity

Image-based techniques for estimating river surface velocity include traditional Large-Scale Particle Image Velocimetry (LSPIV), which infers tracer displacement by computing cross-correlation between pairs of consecutive frames, and optical flow approaches, which derive motion from image intensity gradients. Their accuracy strongly depends on image quality and processing configurations. However, the influence of enhancement techniques and parameter settings on large-scale velocimetry has not yet been systematically explored, due to complex interactions between field conditions, enhancement methods, and software-specific options. As a result, most studies still rely on authors’ experience and the processing tools available in the selected software. This study presents a sensitivity analysis evaluating the impact of different combinations of graphical enhancement filters and software parameterizations under varying environmental conditions. Image-based measurements were performed in two case studies in Sicily (Italy): the Oreto and Platani rivers. Acquired videos were pre-processed applying enhancement filters and multiple parameter configurations were tested. Analyses were performed using PIVlab and SSIMS-Flow, each tested with distinct processing setups. Overall, 1296 surface velocity fields were derived and compared to ADCP reference data. Water clarity and background complexity were objectively quantified using the Normalized Difference Turbidity Index (NDTI). Results demonstrate that both filter selection and software settings significantly influence image velocimetry accuracy. PIVlab, relying on cross-correlation, showed greater robustness in visually complex scenarios, while SSIMS-Flow, based on intensity gradients, achieved higher accuracy in uniform and turbid conditions. Poor performance generally corresponded to unsuitable filter-parameter combinations. Overall, the proposed framework helps refine image-based velocimetry workflows for more accurate field implementations.