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 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.
Highlights
Unmanned aerial vehicles (UAVs) are increasingly used for topographic mapping (e.g., Anders et al, 2019)
We demonstrate that combining co-alignment with ground control points (GCPs) leads to the most accurate topographic change detection, outperforming the classical approach by a factor 4, and that co-alignment improves the accuracy of poorly aligned surveys in the dataset
Our results show that co-aligning multiple surveys through UAVSfM leads to more accurate topographic change detection compared to the classical approach where each survey is processed individually, as previously found by Feurer and Vinatier (2018) and Cook and Dietze (2019)
Summary
Unmanned aerial vehicles (UAVs) are increasingly used for topographic mapping (e.g., Anders et al, 2019). High-resolution Digital Surface Models (DSMs) can be created from UAV imagery with unprecedented accuracy and low costs from structure-from-motion (SfM) techniques (Westoby et al, 2012; Fonstad et al, 2013). Effective change detection requires repeated surveys of an area of interest at the relevant geomorphic time scale, sufficient accuracy and precision to resolve changes of the relevant magnitude, and a consistent reference frame for accurate comparison (Cook, 2017). The recent advances in UAV-SfM techniques have made it possible to meet these criteria at relatively low cost and time demands, resulting in a surge of UAV-SfM based geomorphic change detection studies (e.g., Eltner et al, 2016; Anders et al, 2019). Especially for topographic change detection it is of key importance that the differenced DSMs are both accurate and spatially consistent (Feurer and Vinatier, 2018)
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