Measuring river planform changes from remotely sensed data – a Monte Carlo approach to assessing the impact of spatially variable error

Jautzy, Timothée; Herrault, Pierre-Alexis; Chardon, Valentin; Schmitt, Laurent; Rixhon, Gilles

Remotely sensed data from fluvial systems are extensively used to document historical planform changes. However, geometric and delineation errors inherently associated with these data can result in poor or even misleading interpretation of measured changes, especially rates of channel lateral migration. It is thus imperative to take into account a spatially variable (SV) error affecting the remotely sensed data. In the wake of recent key studies using this SV error as a level of detection, we introduce a new framework to evaluate the significance of measured channel migration. Going beyond linear metrics (i.e. migration vectors between diachronic river centrelines), we assess significance through a channel polygon method yielding a surficial metric (i.e. quantification of eroded, deposited, or eroded-then-deposited surfaces).

Our study area is a mid-sized active wandering river: the lower Bruche, a inline-formula∼20 m wide tributary of the Rhine in eastern France. Within our four test sub-reaches, the active channel is digitised using diachronic orthophotos (1950 and 1964), and the SV error affecting the data is interpolated with an inverse-distance weighting (IDW) technique. The novelty of our approach arises from then running Monte Carlo (MC) simulations to randomly translate active channels and propagate geometric and delineation errors according to the SV error. This eventually leads to the computation of percentage of uncertainties associated with each of the measured planform changes, which allows us to evaluate the significance of the planform changes. In the lower Bruche, the uncertainty associated with the documented changes ranges from 15.8 % to 52.9 %.

Our results show that (i) orthophotos are affected by a significant SV error; (ii) the latter strongly affects the uncertainty of measured changes; and (iii) the significance of changes is dependent on both the magnitude and the shape of the surficial changes. Taking the SV error into account is strongly recommended even in orthorectified aerial photos, especially in the case of mid-sized rivers (inline-formula<30 m width) and/or low-amplitude river planform changes (inline-formula<1inline-formula M4inlinescrollmathml unit normal m normal 2 0.125emnobreak normal m - normal 1 0.125emnobreak normal yr - normal 1 58pt15ptsvg-formulamathimg85a034ebd7c863dda4b57ddcf6e96e09 esurf-8-471-2020-ie00001.svg58pt15ptesurf-8-471-2020-ie00001.png ). In addition to allowing detection of low-magnitude planform changes, our approach is also transferable as we use well-established tools (IDW and MC): this opens new perspectives in the fluvial context (e.g. multi-thread river channels) for robustly assessing surficial channel changes.

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Jautzy, Timothée / Herrault, Pierre-Alexis / Chardon, Valentin / et al: Measuring river planform changes from remotely sensed data – a Monte Carlo approach to assessing the impact of spatially variable error. 2020. Copernicus Publications.

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Rechteinhaber: Timothée Jautzy et al.

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