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. Wood is an essential component of rivers and plays a significant role inecology and morphology. It can be also considered a risk factor in riversdue to its influence on erosion and flooding. Quantifying and characterizingwood fluxes in rivers during floods would improve our understanding of thekey processes but are hindered by technical challenges. Among varioustechniques for monitoring wood in rivers, streamside videography is apowerful approach to quantify different characteristics of wood in rivers,but past research has employed a manual approach that has many limitations.In this work, we introduce new software for the automatic detection of woodpieces in rivers. We apply different image analysis techniques such asstatic and dynamic masks, object tracking, and object characterization tominimize false positive and missed detections. To assess the softwareperformance, results are compared with manual detections of wood from thesame videos, which was a time-consuming process. Key parameters that affectdetection are assessed, including surface reflections, lighting conditions,flow discharge, wood position relative to the camera, and the length of woodpieces. Preliminary results had a 36 % rate of false positive detection,primarily due to light reflection and water waves, but post-processingreduced this rate to 15 %. The missed detection rate was 71 % of piecenumbers in the preliminary result, but post-processing reduced this error toonly 6.5 % of piece numbers and 13.5 % of volume. The high precision ofthe software shows that it can be used to massively increase the quantity ofwood flux data in rivers around the world, potentially in real time. Thesignificant impact of post-processing indicates that it is necessary totrain the software in various situations (location, time span, weatherconditions) to ensure reliable results. Manual wood detections andannotations for this work took over 150 labor hours. In comparison, thepresented software coupled with an appropriate post-processing stepperformed the same task in real time (55 h) on a standard desktop computer.

Automated quantification of floating wood pieces in rivers from video monitoring: a new software tool and validation