@article {31607,
	title = {Observer-bias and sampling uncertainties in wood flux and volume estimation from Stream-side video monitoring technique},
	journal = {Earth Surface Processes and Landforms},
	year = {2022},
	abstract = {Wood is an integral part of rivers that can have both positive and negative impacts on natural systems and infrastructures. Different techniques have been developed to quantify wood flux or discharge in rivers. Among them, the stream-side video monitoring technique has proven effective for at-a-station wood monitoring with a high temporal and spatial resolution over an indefinite time period. However, the visual annotation of wood pieces in the videos is subject to uncertainties due to observer bias or {\textquoteleft}vision limitations{\textquoteright}, and video sampling or {\textquoteleft}time limitations. Vision limitations mean that there are patches in the recorded image that may or may not be considered as wood pieces depending on the judgment of the observer. Time limitations mean that the video record may be sampled to estimate the wood flux rather than completing a census of the full record due to the time-consuming nature of continuous visual annotation. To assess these uncertainties, 6 flood events and 13 video segments corresponding to more than 37 days and 64000 pieces of wood were analyzed on two different rivers (Ain and Allier Rivers in France). The results show that while there is a significant difference between observers for the detection of small wood pieces (\&lt;1m in length), no significant difference exists for the detection of large wood pieces (\&gt;1m in length). The application of a truncation length (i.e., considering only wood pieces with a size higher than a certain threshold) reduces the piece number uncertainty significantly without resulting in a meaningful change in the total volume of wood. For the time limitation, it is shown that sampling uncertainty depends on wood flux related to water discharge and flood stages (rising versus falling), so a dynamic sampling strategy that depends on flood stage is recommended.},
	author = {H. Ghaffarian and B. MacVicar and B. Hortobagyi and Z. Zhang and F. Robert and L. Vaudor and S. Petit and H. Pi{\'e}gay}
}
