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Earth Surface Processes and Landforms
Article . 2020 . Peer-reviewed
License: CC BY
Data sources: Crossref
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Earth Surface Processes and Landforms
Article
License: CC BY
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Serveur académique lausannois
Article . 2020
License: CC BY
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Mitigating systematic error in topographic models for geomorphic change detection: accuracy, precision and considerations beyond off‐nadir imagery

Authors: Mike R. James; Gilles Antoniazza; Stuart Robson; Stuart N. Lane;

Mitigating systematic error in topographic models for geomorphic change detection: accuracy, precision and considerations beyond off‐nadir imagery

Abstract

Abstract Unmanned aerial vehicles (UAVs) and structure‐from‐motion photogrammetry enable detailed quantification of geomorphic change. However, rigorous precision‐based change detection can be compromised by survey accuracy problems producing systematic topographic error (e.g. ‘doming’), with error magnitudes greatly exceeding precision estimates. Here, we assess survey sensitivity to systematic error, directly correcting topographic data so that error magnitudes align more closely with precision estimates. By simulating conventional grid‐style photogrammetric aerial surveys, we quantify the underlying relationships between survey accuracy, camera model parameters, camera inclination, tie point matching precision and topographic relief, and demonstrate a relative insensitivity to image overlap. We show that a current doming‐mitigation strategy of using a gently inclined (<15°) camera can reduce accuracy by promoting a previously unconsidered correlation between decentring camera lens distortion parameters and the radial terms known to be responsible for systematic topographic error. This issue is particularly relevant for the wide‐angle cameras often integrated into current‐generation, accessible UAV systems, frequently used in geomorphic research. Such systems usually perform on‐board image pre‐processing, including applying generic lens distortion corrections, that subsequently alter parameter interrelationships in photogrammetric processing (e.g. partially correcting radial distortion, which increases the relative importance of decentring distortion in output images). Surveys from two proglacial forefields (Arolla region, Switzerland) showed that results from lower‐relief topography with a 10°‐inclined camera developed vertical systematic doming errors > 0·3 m, representing accuracy issues an order of magnitude greater than precision‐based error estimates. For higher‐relief topography, and for nadir‐imaging surveys of the lower‐relief topography, systematic error was < 0·09 m. Modelling and subtracting the systematic error directly from the topographic data successfully reduced error magnitudes to values consistent with twice the estimated precision. Thus, topographic correction can provide a more robust approach to uncertainty‐based detection of event‐scale geomorphic change than designing surveys with small off‐nadir camera inclinations and, furthermore, can substantially reduce ground control requirements. © 2020 The Authors. Earth Surface Processes and Landforms published by John Wiley & Sons Ltd

Country
United Kingdom
Keywords

550, decentring lens distortion, UAV, topographic correction, systematic doming error, DEM, Earth-Surface Processes; Earth and Planetary Sciences (miscellaneous); Geography, Planning and Development, structure‐from‐motion

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    139
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    Top 1%
    influence
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citations
The total number of citations received by all articles of the researcher of interest.
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
139
Top 1%
Top 10%
Top 1%
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