Preprints
https://doi.org/10.5194/esurf-2020-43
https://doi.org/10.5194/esurf-2020-43

  16 Jun 2020

16 Jun 2020

Review status: this preprint has been withdrawn by the authors.

Short Communication: Optimizing UAV-SfM based topographic change detection with survey co-alignment

Tjalling de Haas1, Wiebe Nijland1, Brian W. McArdell2, and Maurice W. M. L. Kalthof1 Tjalling de Haas et al.
  • 1Department of Physical Geography, Universiteit Utrecht, Utrecht, 3508 CB, the Netherlands
  • 2Swiss Federal Institute for Forest, Snow and Landscape Research WSL, Birmensdorf, 8903, Switzerland

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.

This preprint has been withdrawn.

Tjalling de Haas et al.

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Interactive discussion

Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
Printer-friendly Version - Printer-friendly version Supplement - Supplement

Tjalling de Haas et al.

Tjalling de Haas et al.

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This preprint has been withdrawn.

Short summary
High-quality digital surface models generated by automated photogrammetry techniques on aerial images captured with drones are increasingly used for topographic change detection. We demonstrate that co-aligning the images from multiple surveys strongly enhances the accuracy of topographic change detection. We find that co-alignment leads to particularly large improvements in the accuracy of poorly aligned surveys that have severe offsets when processed individually.