Accounting for uncertainty in remotely-sensed measurements of river planform change

Donovan, Mitchell; Belmont, Patrick; Notebaert, Bastiaan; Coombs, Tyrel; Larson, Phillip H.; Souffront, Michael

doi:10.1016/j.earscirev.2019.04.009

Cited by 43 publications

(65 citation statements)

References 80 publications

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“…MC are also relatively easy to perform and applicable in very different cases. This could improve the generalisation of methods for calculating 5 planform changes and spatially variable uncertainty in a fluvial context, as suggested by Donovan et al (2019).…”

Section: Channel Boundaries Simulation Methodsmentioning

confidence: 98%

“…Last, as mentioned by Donovan et al (2019), it is quite hard to distinguish errors inherent to coregistrating and digitising process. For this reason, a digitising error (e d ) equal to 1 pixel was added as a reasonable constraint within the simulation 20 process, considering the resolution of the orthophotos.…”

Section: Channel Boundaries Simulation Methodsmentioning

confidence: 99%

“…However, two major sources of spatial uncertainties inherently question the robustness of these planimetric methods: the delineation error due to digitisation of river banks Güneralp et al, 2014;Gurnell et al, 1994;Micheli and Kirchner, 2002;Werbylo et al, 2017) and the geometric error due to data coregistration (Gaeuman et al, 2005;Hughes et al, 2006;Liébault and Piégay, 2001;Payraudeau et al, 2010;Swanson et al, 2011). Whatever the scope of the study and 15 the environmental context, these uncertainties needs to be assessed as accurately as possible (De Rose and Basher, 2011;Donovan et al, 2019;Mount and Louis, 2005;Mount et al, 2003). In that way, the Root Mean Square Error (RMSE) has been frequently used over the last decades to assess a uniform geometric error affecting coregistrated planimetric data (Table 1).…”

Section: Introductionmentioning

confidence: 99%

“…To test its impact on the quantification of lateral migration, the SV-error was used 20 as a SV Level of Detection (LoD): it allowed detecting 33 % of statistically significant changes (migrations) instead of only 24 % with the RMSE/uniform error approach (Lea and Legleiter, 2016). The thorough review of Donovan et al (2019) lately reached the same conclusion: they encouraged the generalisation of SV-error assessment and also notified the potential need (for instance in the case of complex planforms such as braided rivers) for testing SV-LoD on new metrics of lateral migration, such as surfacic ones.…”

Section: Introductionmentioning

confidence: 99%

“…Both Lea and Legleiter (2016) and Donovan et al (2019) developed a LoD on a linear metric ( Fig. 1a) implemented in the Planform Statistics Toolbox (Lauer, 2006), which reports fluvial planform changes as a linear adjustment.…”

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confidence: 99%

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Short communication: Significance assessment of historical surfacic planform changes of mid-sized rivers: A Monte-Carlo based approach

Jautzy

Herrault

Chardon

et al. 2019

Preprint

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Abstract. Remote-sensed data in the fluvial context 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 fundamental to take a spatially-variable (SV) error affecting remote-sensed data into account. 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 their linear metric (i.e. migration vectors between diachronic river centrelines), we assess this significance through the channel polygon method yielding a surfacic metric (i.e. quantification of eroded, deposited, or eroded/deposited surfaces). Our study area is an active wandering mid-sized river: the lower Bruche, a ∼ 20 m wide sub-tributary of the Rhine in eastern France. Within our four test sub-reaches, the active channel is digitised using diachronic orthophotos (1950; 1964) and the sub-reach specific SV-error affecting the data is interpolated with an Inverse Distance Weighting (IDW) technique. A main novelty of our approach consists then in 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 production of a Surface of Detection (SoD), which allows evaluating the significance of measured surfacic changes. Putting the SoD into practice in the lower Bruche shows that only 37 % of the total surfacic measured changes are significant. Our results suggest that (i) orthophotos are affected by a significant SV-error, (ii) the latter strongly affects the significance of measured changes and (iii) the significance is strongly dependent on the magnitude of surfacic changes. Taking the SV-error into account is strongly recommended, regardless of the remote-sensed data used (orthophotos or aerial photos), especially in the case of mid-sized rivers (

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Section: Channel Boundaries Simulation Methodsmentioning

confidence: 98%

Section: Channel Boundaries Simulation Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

See 3 more Smart Citations

Short communication: Significance assessment of historical surfacic planform changes of mid-sized rivers: A Monte-Carlo based approach

Jautzy

Herrault

Chardon

et al. 2019

Preprint

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Measuring channel planform change from image time series: A generalizable, spatially distributed, probabilistic method for quantifying uncertainty

Leonard

Legleiter

Lea

et al. 2020

Earth Surf Processes Landf

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Channels change in response to natural or anthropogenic fluctuations in streamflow and/or sediment supply and measurements of channel change are critical to many river management applications. Whereas repeated field surveys are costly and time‐consuming, remote sensing can be used to detect channel change at multiple temporal and spatial scales. Repeat images have been widely used to measure long‐term channel change, but these measurements are only significant if the magnitude of change exceeds the uncertainty. Existing methods for characterizing uncertainty have two important limitations. First, while the use of a spatially variable image co‐registration error avoids the assumption that errors are spatially uniform, this type of error, as originally formulated, can only be applied to linear channel adjustments, which provide less information on channel change than polygons of erosion and deposition. Second, previous methods use a level‐of‐detection (LoD) threshold to remove non‐significant measurements, which is problematic because real changes that occurred but were smaller than the LoD threshold would be removed. In this study, we present a new method of quantifying uncertainty associated with channel change based on probabilistic, spatially varying estimates of co‐registration error and digitization uncertainty that obviates a LoD threshold. The spatially distributed probabilistic (SDP) method can be applied to both linear channel adjustments and polygons of erosion and deposition, making this the first uncertainty method generalizable to all metrics of channel change. Using a case study from the Yampa River, Colorado, we show that the SDP method reduced the magnitude of uncertainty and enabled us to detect smaller channel changes as significant. Additionally, the distributional information provided by the SDP method allowed us to report the magnitude of channel change with an appropriate level of confidence in cases where a simple LoD approach yielded an indeterminate result. © 2020 John Wiley & Sons, Ltd.

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Predominant landward skewing of tidal meanders

Gao

Finotello

Wang

2022

Earth Surf Processes Landf

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Accounting for uncertainty in remotely-sensed measurements of river planform change

Cited by 43 publications

References 80 publications

Short communication: Significance assessment of historical surfacic planform changes of mid-sized rivers: A Monte-Carlo based approach

Short communication: Significance assessment of historical surfacic planform changes of mid-sized rivers: A Monte-Carlo based approach

Measuring channel planform change from image time series: A generalizable, spatially distributed, probabilistic method for quantifying uncertainty

Predominant landward skewing of tidal meanders

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