On the use of LSPIV-based free software programs for the monitoring of river: testing the PIVlab and the FUDAA-LSPIV with synthetic and real image sequences

• DOI: 10.5194/egusphere-egu2020-10155
• Publication Date: 2020-03-09T21:42:26Z
• Authors: Dario Pumo; Francesco Alongi; Giuseppe Ciraolo; Leonardo Noto
• Keywords: PIVlab; LSPIV; river monitoring;PIVlab; FUDAA-LSPIV;synthetic image sequences; field survey; 13. Climate action; FUDAA-LSPIV; Settore ICAR/02 - Costruzioni Idrauliche E Marittime E Idrologia; synthetic image sequences; LSPIV; river monitoring; field survey

<p>The development of new image-based techniques is allowing a radical change in the environmental monitoring field. The fundamental characteristics these methods are related to possibility obtaining non-intrusive measurements even adverse circumstances, such as high flow conditions, which may seriously threaten operators’ safety conditions traditional approaches.</p><p>Optical techniques, based on acquisition, analysis and elaboration sequences images acquired by digital cameras, aimed at complete characterization river instantaneous surface velocity field, through floating tracer, be naturally present or artificially introduced. growing availability generation both low-cost optical sensors high-performing free software programs for image processing, key aspect explaining rapid recent years. best known large scale particle velocimetry (LSPIV) tracking (LSPTV).</p><p>This work analyze compare performance two most common packages LSPIV (i.e. PIVlab FUDAA-LSPIV), use different cross-correlation algorithms. test carried out analyzing several synthetic real frames natural rivers under (with tracers introduced). An generator has been implemented ad-hoc with aim create, fixed configurations, images, simulating uniformly distributed moving controlled conditions. various configurations characterized parameterization terms of: (i) (S=slow F=fast according logarithmic transverse profile); (ii) tracer particles size (CON= disks constant diameter; VAR=disks variable given mean diameter); (iii) seeding density per frame (density: low -LD, medium -MD, -HD).</p><p>The processed together sequences, errors value field along transect, respect benchmark that imposed sequence deriving from sensors, i.e. ADCP, sequences).</p>