the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Short Communication: Optimizing UAV-SfM based topographic change detection with survey co-alignment
Abstract. High-quality digital surface models (DSMs) generated from structure-from-motion (SfM) based on imagery captured from unmanned aerial vehicles (UAVs), are increasingly used for topographic change detection. Classically, DSMs were generated for each survey individually and then compared to quantify topographic change, but recently it was shown that co-aligning the images of multiple surveys may enhance the accuracy of topographic change detection. Here, we use nine surveys over the Illgraben debris-flow torrent in the Swiss Alps to compare the accuracy of three approaches for UAV-SfM topographic change detection: (1) the classical approach where each survey is processed individually using ground control points (GCPs), (2) co-alignment of all surveys without GCPs, and (3) co-alignment of all surveys with GCPs. We demonstrate that compared to the classical approach co-alignment enhances the accuracy of topographic change detection by a factor 4 with GCPs and a factor 3 without GCPs, leading to xy and z offsets < 0.1 m for both co-alignment approaches. We further show that co-alignment leads to particularly large improvements in the accuracy of poorly aligned surveys that have severe offsets when processed individually, by forcing them onto the more accurate common geometry set by the other surveys. Based on these results we advocate that co-alignment, preferably with GCPs, should become the common-practice in high-accuracy UAV-SfM topographic change detection studies.
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Interactive discussion
- RC1: 'Review', Anonymous Referee #1, 18 Jul 2020
- RC2: 'Review of de Haas et al.', Benjamin Purinton, 10 Aug 2020
- RC3: 'referee comment', Anonymous Referee #3, 11 Aug 2020
- EC1: 'editor comment', Wolfgang Schwanghart, 11 Aug 2020
- AC1: 'Author response', Tjalling de Haas, 02 Oct 2020
Interactive discussion
- RC1: 'Review', Anonymous Referee #1, 18 Jul 2020
- RC2: 'Review of de Haas et al.', Benjamin Purinton, 10 Aug 2020
- RC3: 'referee comment', Anonymous Referee #3, 11 Aug 2020
- EC1: 'editor comment', Wolfgang Schwanghart, 11 Aug 2020
- AC1: 'Author response', Tjalling de Haas, 02 Oct 2020
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Cited
2 citations as recorded by crossref.
- Assessing DEM quality and minimizing registration error in repeated geomorphic surveys with multi‐temporal ground truths of invariant features: Application to a long‐term dataset of beach topography and nearshore bathymetry S. Bertin et al. 10.1002/esp.5436
- A comparative study of machine learning algorithms for sediment classification in debris flow fans using UAV imagery: a case study in the Ohya landslide scar, Japan S. Yousefi & F. Imaizumi 10.1007/s10346-024-02402-w