<div> <p><span>Large wood (10cm diameter & 1m long) gets recruited into a mountain river system from surrounding forested areas. Instream large wood positively influences the diversity of the river system, creating habitats for terrestrial and aquatic species. However, the corresponding risk to the presence of instream large wood is a more controversial topic in river management. On the one hand, large wood increases the riverbed roughness, partly dissipating energy during a flood. On the other hand, its transport during floods might cause damage to infrastructure. Direct observations or monitoring stations are scarce and knowledge on how and when wood is transported remains far from complete.</span><span><br></span></p> </div><div> <p><span>In order to quantify a river’s instream wood transport regime, w</span><span>e are developing a video-based wood tracking system that counts the number of pieces that pass a certain point and estimates their sizes. We use a DeepSORT algorithm that uses machine learning to identify individual pieces of instream wood and draws a bounding box around every piece. Subsequently, it uses a Kalman filter to estimate the piece’s trajectory. To prevent counting the same pieces multiple times, the projected trajectory is compared to the detections in the subsequent frame. The system is designed so that it can be applied to different datasets and will be available to the increasing wood monitoring efforts around the world. For a more detailed look into the large wood regime at one of our main study sites (Vallon de Nant, Switzerland), and to calibrate our video-based wood tracking system, we have installed RFID tags into all pieces of large wood (approximately 1000 pieces) over a stretch of 3 km. A stationary RFID antenna registers the tagged pieces that pass by, of which the size and origin are known.</span><span> <br></span></p> </div><div> <p><span>First results show that the custom trained DeepSORT algorithm can not only identify pieces of instream wood, but also largely follow the pieces in subsequent frames. The approach seems to outperform current computer vision solutions. In our ongoing research, we aim to make the system more robust and expand the observation network to other rivers. With an expanding dataset, containing (manually) labelled training samples from different locations, and the low-cost measurement set-up, the system promises to aid successfully to an intercomparison of river systems in the context of the wood management debate.</span><span><br></span></p> </div><div> <p><span>This work is supported by the SNSF Eccellenza project PCEFP2-186963 and the University of Lausanne.</span></p> </div>

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<div> <div> <p><span data-contrast="auto">By creating pools and retaining sediment and organic matter, instream wood provides habitats for a vast variety of different species. It creates a complex river bed and is essential for a healthy ecosystem (Wohl et al., 2019). However, during extreme weather conditions, floods can mobilize the wood and transport it, causing a hazard to downstream infrastructure. Therefore it is important better understand river wood dynamics, such as storage and transport regimes. These regimes are influences by individual log characteristics (e.g. shape, density and orientation), but also individual river weather, climate and geographical factors. In the last decade, an increasing amount of case studies have been performed, although still limited in amount of logs tracked in European rivers (Wyzga et al., 2017). In our current contribution, we deploy a tracking and monitoring system in an Alpine river in the canton of Vaud, Switzerland. The Avancon the Nant is located in the Vallon de Nant, a valley that has been protected since 1969 (Vittoz and Gmür, 2009), and can therefore be argued to have a close to natural wood regime.</span></p> <p><img src="" alt="" /></p> <div> <div> <p><span data-contrast="auto">Figure: Locations of instream wood in 2022 as compared to 2021. In grey, 3 special sections (wider sections and sections with multiple streams) of river are represented.</span></p> </div> </div> <div> <div> <p><span data-contrast="auto">In the summer of 2021, 948 (0001 to 0948) pieces of instream wood were tagged with a unique number and 2 unique RFID tags. One year later, in another field campaign, the movement of the pieces was assessed (see figure). From the pieces that have been recovered (7% were lost), a total of 20 pieces were found to have moved with an average of 260 meters. These movements took place in specific sections, primarily in single-threaded narrow sections. The two lower special river sections (w1 and w2) were found to contain pieces with a larger diameters as compared to the other sections. As the tree density decreases when moving up the river, also the total volume of wood storage and the amount of pieces decreased. Furthermore, more pieces with a high degree of decat were found as compared to fresher pieces. This indicated that in recent years, less wood recruitment has taken place.</span><span data-ccp-props="{"> </span></p> </div> <div> <p><span data-contrast="auto">REFERENCES </span><span data-ccp-props="{"> </span></p> </div> <div> <p><span data-contrast="auto">Vittoz, P., & Gmür, P. 2009: Introduction aux Journées de la biodiversité dans le Vallon de Nant (Bex, Alpes vaudoises), </span><span data-contrast="auto">Mémoire de la Société vaudoise des Sciences naturelles, 23, 3-20.</span><span data-ccp-props="{"> </span></p> </div> <div> <p><span data-contrast="auto">Wohl, E., Kramer, N., Ruiz-Villanueva, V., Scott, D. N., Comiti, F., Gurnell, A. M., Piegay, H., Lininger, K. B., Jaeger, K. L., Walters, D. M., & Fausch, K. D. 2019: The natural wood regime in rivers, BioScience, 69, 259–273.</span><span data-ccp-props="{"> </span></p> </div> <div> <p><span data-contrast="auto">Wyzga, B., Mikus, P., Zawiejska, J., Ruiz-Villanueva, V., Kaczka, R. J. & Czech, W. 2017: Log transport and deposition in incised, channelized, and multithread reaches of a wide mountain river: Tracking experiment during a 20-year flood, Geomorphology, 279, 98-111.</span><span data-ccp-props="{"> </span></p> </div> </div> </div> </div>