Investigating the accuracy of photointerpreted unvegetated channel widths in a braided river system: a Platte River case study

Werbylo, Kevin L.; Farnsworth, Jason M.; Baasch, David M.; Farrell, Patrick D.

doi:10.1016/j.geomorph.2016.11.003

Cited by 19 publications

(12 citation statements)

References 10 publications

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“…We suspect that interpretation uncertainty will be high in rivers that experience a large change in wetted channel area given a proportionately small change in discharge (e.g. braided rivers), because low‐elevation bars are frequently wetted but not scoured, which allows fast‐growing vegetation to encroach on these surfaces (Werbylo et al ., 2017). In such rivers, vegetation density is a poor proxy for the active channel, and the digitizer must use professional judgement in placing the active channel boundary.…”

Section: Discussionmentioning

confidence: 99%

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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Section: Discussionmentioning

confidence: 99%

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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“…Pearse et al (2017) estimated unobstructed channel width from the perspective of cranes, determining it as the distance across the channel, including bare soil and vegetation under 1.5 m in height. Werbylo et al (2017) considered unvegetated channel width as including sandbars with <25% vegetative cover. For the purposes of this study, we considered the "unobstructed channel width" as the distance from bank to opposite bank, including areas of active flow and unstabilized sandbars.…”

Section: Habitat Assessmentmentioning

confidence: 99%

“…We manually measured "total unobstructed channel width" (UOCW) in ArcGIS 10.5.1 by broadly following the techniques of Werbylo et al (2017;Fig. 3).…”

Section: Habitat Assessmentmentioning

confidence: 99%

Temporospatial Shifts in Sandhill Crane Staging in the Central Platte River Valley in Response to Climatic variation and Habitat Change

Caven¹,

Buckley

King

et al. 2020

Monographs of the Western North American Naturalist

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“…Aerial photographs are thus commonly used to document and measure planform channel changes of at the most the last century, in a wide variety of fluvial settings. Requiring data coregistration and river bank digitisation, these planimetric studies generally result in the extraction of morphological metrics such as channel width (Gilvear, 2004;Werbylo et al, 2017;Winterbottom, 2000) or lateral migration (Hooke and Yorke, 2010;Janes et al, 2017;Mandarino et al, 2019;O'Connor et al, 2003) to characterise their evolution in time (e.g. rates of lateral migration).…”

Section: Introductionmentioning

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).…”

Section: Introductionmentioning

confidence: 99%

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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Investigating the accuracy of photointerpreted unvegetated channel widths in a braided river system: a Platte River case study

Cited by 19 publications

References 10 publications

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

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

Temporospatial Shifts in Sandhill Crane Staging in the Central Platte River Valley in Response to Climatic variation and Habitat Change

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

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