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Automated Bedform Identification—A Meta‐Analysis of Current Methods and the Heterogeneity of Their Outputs.

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Title: Automated Bedform Identification—A Meta‐Analysis of Current Methods and the Heterogeneity of Their Outputs.
Authors: Scheiber, Leon1 (AUTHOR) scheiber@lufi.uni-hannover.de, Zomer, Judith2 (AUTHOR), Wang, Li3 (AUTHOR), Cisneros, Julia4 (AUTHOR), Gutierrez, Ronald R.5 (AUTHOR), Lefebvre, Alice6 (AUTHOR)
Source: Journal of Geophysical Research. Earth Surface. Mar2024, Vol. 129 Issue 3, p1-19. 19p.
Subject Terms: *Sand dunes, Oceanographic maps, Distribution (Probability theory), Heterogeneity
Abstract: Ongoing efforts to characterize underwater dunes have led to a considerable number of freely available tools that identify these bedforms in a (semi‐)automated way. However, these tools differ with regard to their research focus and appear to produce results that are far from unequivocal. We scrutinize this assumption by comparing the results of five recently published dune identification tools in a comprehensive meta‐analysis. Specifically, we analyze dune populations identified in three bathymetries under diverse flow conditions and compare the resulting dune characteristics in a quantitative manner. Besides the impact of underlying definitions, it is shown that the main heterogeneity arises from the consideration of a secondary dune scale, which has a significant influence on statistical distributions. Based on the quantitative results, we discuss the individual strengths and limitations of each algorithm, with the aim of outlining adequate fields of application. However, the concerted bedform analysis and subsequent combination of results have another benefit: the creation of a benchmarking data set which is inherently less biased by individual focus and therefore a valuable instrument for future validations. Nevertheless, it is apparent that the available tools are still very specific and that end‐users would profit by their merging into a universal and modular toolbox. Plain Language Summary: In this paper, we present a comparison of several recently published bedform analysis tools that were created to measure the size and shape of underwater dunes. We compare these tools and how they performed on three different seabed elevation maps that detail dunes in a river, an estuary, and a laboratory flume. We focus on the resulting measurements of dune morphology, such as dune height and length. We show that the consideration of a secondary dune scale has a significant influence on the statistical distributions that describe the measured dune populations. With this knowledge, we offer a discussion of the strengths and limitations of each tool and examples of its proper application. We also offer the combination of the measurements from each tool as a benchmarking dataset that can be used for future tool creation and method validation. Finally, we recommend that each tool be used with specific needs in mind and a universal and modular toolbox should be created that incorporates all available options for dune identification. Key Points: Five recent dune identification tools are compared in a meta‐analysis assessing three bathymetries from diverse flow environmentsResults differ in sampling size, computation time and dune characteristics, but mainly regarding the allowance of small‐scale bedformsThe combination of results can be used as benchmarking data in future validations because it is less biased by individual research focus [ABSTRACT FROM AUTHOR]
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Abstract:Ongoing efforts to characterize underwater dunes have led to a considerable number of freely available tools that identify these bedforms in a (semi‐)automated way. However, these tools differ with regard to their research focus and appear to produce results that are far from unequivocal. We scrutinize this assumption by comparing the results of five recently published dune identification tools in a comprehensive meta‐analysis. Specifically, we analyze dune populations identified in three bathymetries under diverse flow conditions and compare the resulting dune characteristics in a quantitative manner. Besides the impact of underlying definitions, it is shown that the main heterogeneity arises from the consideration of a secondary dune scale, which has a significant influence on statistical distributions. Based on the quantitative results, we discuss the individual strengths and limitations of each algorithm, with the aim of outlining adequate fields of application. However, the concerted bedform analysis and subsequent combination of results have another benefit: the creation of a benchmarking data set which is inherently less biased by individual focus and therefore a valuable instrument for future validations. Nevertheless, it is apparent that the available tools are still very specific and that end‐users would profit by their merging into a universal and modular toolbox. Plain Language Summary: In this paper, we present a comparison of several recently published bedform analysis tools that were created to measure the size and shape of underwater dunes. We compare these tools and how they performed on three different seabed elevation maps that detail dunes in a river, an estuary, and a laboratory flume. We focus on the resulting measurements of dune morphology, such as dune height and length. We show that the consideration of a secondary dune scale has a significant influence on the statistical distributions that describe the measured dune populations. With this knowledge, we offer a discussion of the strengths and limitations of each tool and examples of its proper application. We also offer the combination of the measurements from each tool as a benchmarking dataset that can be used for future tool creation and method validation. Finally, we recommend that each tool be used with specific needs in mind and a universal and modular toolbox should be created that incorporates all available options for dune identification. Key Points: Five recent dune identification tools are compared in a meta‐analysis assessing three bathymetries from diverse flow environmentsResults differ in sampling size, computation time and dune characteristics, but mainly regarding the allowance of small‐scale bedformsThe combination of results can be used as benchmarking data in future validations because it is less biased by individual research focus [ABSTRACT FROM AUTHOR]
ISSN:21699003
DOI:10.1029/2023JF007607