Preprints
https://doi.org/10.5194/esurf-2020-96
https://doi.org/10.5194/esurf-2020-96

  20 Nov 2020

20 Nov 2020

Review status: this preprint is currently under review for the journal ESurf.

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

Hossein Ghaffarian1, Pierre Lemaire1,2, Zhang Zhi1, Laure Tougne2, Bruce MacVicar3, and Hervé Piégay1 Hossein Ghaffarian et al.
  • 1Univ. Lyon, UMR 5600, Environnement-Ville-Société CNRS, F-69362 Lyon, France
  • 2Univ. Lyon, UMR 5205, Laboratoire d'InfoRmatique en Image et Systèmes d'information CNRS, F-69676 Lyon, France
  • 3Department of Civil and Environmental Engineering, Univ. Waterloo, Waterloo, Ontario, Canada

Abstract. Wood is an essential component of rivers and plays a significant role in ecology and morphology. It can be also considered as a risk factor in rivers due to its influence on erosion and flooding. Quantifying and characterizing wood fluxes in rivers during floods would improve our understanding of the key processes but is hindered by technical challenges. Among various techniques for monitoring wood in rivers, streamside videography is a powerful 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 wood pieces in rivers. We apply different image analysis techniques such as static and dynamic masks, object tracking, and object characterization to minimize false positive and missed detections. To assess the software performance, results are compared with manual detections of wood from the same videos, which was a time-consuming process. Key parameters that affect detection are assessed including surface reflections, lighting conditions, flow discharge, wood position relative to the camera, and the length of wood pieces. Preliminary results had a 36 % rate of false-positive detection, primarily due to light reflection and water waves, but post-processing reduced this rate to 15 %. The missed detection rate was 71 % of piece numbers in the preliminary result, but post-processing reduced this error to only 6.5 % of piece numbers, and 13.5 % of volume. The high precision of the software shows that it can be used to massively increase the quantity of wood flux data in rivers around the world, potentially in real-time. The significant impact of post-processing indicates that it is necessary to train the soft-ware in various situations (location, timespan, weather conditions) to ensure reliable results. Manual wood detections and annotations for this work took more than one human-month of labor. In comparison, the presented software coupled with an appropriate post-processing step performed the same task in real-time (55 hr) on a standard desktop computer.

Hossein Ghaffarian et al.

 
Status: final response (author comments only)
Status: final response (author comments only)
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment

Hossein Ghaffarian et al.

Hossein Ghaffarian et al.

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Short summary
Quantifying wood fluxes in rivers would improve our understanding of the key processes in river ecology and morphology. In this work, we introduce new software for the automatic detection of wood pieces in rivers. The results show 93.5 % and 86.5 % accuracy for piece number and volume respectively.