Abstract
AbstractUnmanned 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
Highlights
The use of unmanned aerial vehicles (UAVs) has become an established approach for acquiring centimetre-resolution topographic information through structure-from-motion (SfM) photogrammetry (e.g. Eltner et al, 2015; Harwin and Lucieer, 2012; Hugenholtz et al, 2013; Niethammer et al, 2010; Ouedraogo et al, 2014; Rosnell and Honkavaara, 2012; Turner et al, 2015)
Whereas previous work has focussed on doming mitigation through improving the camera model (Carbonneau and Dietrich, 2017; James and Robson, 2014a; Wackrow and Chandler, 2011), we demonstrate an alternative approach, in which systematic topographic error is modelled and removed directly from results, minimizing bias and aligning error magnitudes more closely to those expected from precision estimates
For UAV-based SfMphotogrammetry surveys, which are developing into a leading source of topographic data in geomorphology, we quantify survey sensitivities to accuracy issues and identify a potential vulnerability resulting from the emerging trend of on-camera image pre-processing
Summary
The use of unmanned aerial vehicles (UAVs) has become an established approach for acquiring centimetre-resolution topographic information through structure-from-motion (SfM) photogrammetry (e.g. Eltner et al, 2015; Harwin and Lucieer, 2012; Hugenholtz et al, 2013; Niethammer et al, 2010; Ouedraogo et al, 2014; Rosnell and Honkavaara, 2012; Turner et al, 2015). Our previous laboratory calibrations of digital Single Lens Reflex (dSLR) cameras and 28 mm lenses for ground-based topographic work (e.g. James et al, 2007; James and Robson, 2014b; James et al, 2006) have shown maximum decentring distortion magnitudes of < 0·5 pixels, around two orders of magnitude smaller than those from radial parameters (~50 pixels). At such magnitudes, decentring components can sometimes be removed from the distortion model because the parameter values cannot be determined as significantly different from zero during calibration (i.e. parameter value magnitudes that do not exceed their measurement precision; e.g. Fraser, 2001; Gruen and Beyer, 2001)
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
