Case Report: Optimization of Topographic Change Detection With UAV Structure-From-Motion Photogrammetry Through Survey Co-Alignment

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 with GCPs leads to a minor and marginally significant increase in absolute accuracy. Moreover, compared to the classical approach co-alignment enhances the relative 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 to ensure a high absolute accuracy, should become common-practice in high-accuracy UAV-SfM topographic change detection studies for projects with sufficient stable areas.