Abstract. Images captured by unmanned aerial vehicles (UAVs) and
processed by structure-from-motion (SfM) photogrammetry are increasingly
used in geomorphology to obtain high-resolution topography data.
Conventional georeferencing using ground control points (GCPs) provides
reliable positioning, but the geometrical accuracy critically depends on the
number and spatial layout of the GCPs. This limits the time and
cost effectiveness. Direct georeferencing of the UAV images with
differential GNSS, such as PPK (post-processing kinematic), may overcome
these limitations by providing accurate and directly georeferenced surveys.
To investigate the positional accuracy, repeatability and reproducibility of
digital surface models (DSMs) generated by a UAV–PPK–SfM workflow, we
carried out multiple flight missions with two different camera–UAV systems:
a small-form low-cost micro-UAV equipped with a high field of view (FOV) action camera and a
professional UAV equipped with a digital single lens reflex (DSLR) camera. Our analysis showed that the
PPK solution provides the same accuracy (MAE: ca. 0.02 m, RMSE: ca. 0.03 m)
as the GCP method for both UAV systems. Our study demonstrated that a
UAV–PPK–SfM workflow can provide consistent, repeatable 4-D data with an
accuracy of a few centimeters. However, a few flights showed vertical bias
and this could be corrected using one single GCP. We further evaluated
different methods to estimate DSM uncertainty and show that this has a large
impact on centimeter-level topographical change detection. The DSM reconstruction
and surface change detection based on a DSLR and action camera were
reproducible: the main difference lies in the level of detail of the surface
representations. The PPK–SfM workflow in the context of 4-D Earth surface
monitoring should be considered an efficient tool to monitor geomorphic
processes accurately and quickly at a very high spatial and temporal
resolution.
Evaluating the potential of post-processing kinematic (PPK) georeferencing for UAV-based structure- from-motion (SfM) photogrammetry and surface change detection
