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Remotely piloted airborne system (RPAS) based structure-from-motion (SfM) photogrammetry is a recognised tool in geomorphological applications. However, time constraints, methodological requirements and ignorance can easily compromise photogrammetric rigour in geomorphological fieldwork. Light RPAS mounted sensors often provide inherent low geometric stability and are thus typically calibrated on-the-job in a self-calibrating bundle adjustment. Solving interior (lens geometry) and exterior (position and orientation) camera parameters requires variation of sensor-object distance, view angles and surface geometry.Deficient camera calibration can cause systematic errors resulting in final digital elevation model (DEM) deformation. The application of multi-sensor systems, common in geomorphological research, poses additional challenges. For example, the low contrast in thermal imagery of vegetated surfaces constrains image matching algorithms.We present a pre-calibration workflow to separate sensor calibration and data acquisition that is optimized for geomorphological field studies. The approach is time-efficient (rapid simultaneous image acquisition), repeatable (permanent object), at survey scale to maintain focal distance, and on-site to avoid shocks during transport.The presented workflow uses a stone building as a suitable 3D calibration structure (alternatively boulder or bridge) providing structural detail in visible (DJI Phantom 4 Pro) and thermal imagery (Workswell WIRIS Pro). The dataset consists of feature coordinates extracted from terrestrial laser scanner (TLS) scans (3D reference data) and imagery (2D calibration data). We process the data in the specialized software, vision measurement system (VMS) as benchmark and the widely applied commercial SfM photogrammetric software, Agisoft MetaShape (AM) as convenient alternative. Subsequently, we transfer the camera parameters to the application in an SfM photogrammetric dataset of a river environment to assess the performance of self- and pre-calibration using different image network configurations. The resulting DEMs are validated against GNSS reference points and by DEMs of difference. We achieved calibration accuracies below one-third (optical) and one-quarter (thermal) of a pixel. In line with the literature, our results show that self-calibration yields the smallest errors and DEM deformations using multi-scale and oblique datasets. Pre-calibration in contrast, yielded the lowest overall errors and performed best in the single-scale nadir scenario. VMS consistently performed better than AM, possibly because AM's software “black-box” is less customisable and does not allow purely marker-based calibration. Furthermore, we present findings regarding sensor stability based on a repeat survey.We find that pre-calibration can improve photogrammetric accuracies in surveys restricted to unfavourable designs e.g. nadir-only (water refraction, sensor mount). It can facilitate the application of thermal sensors on surfaces less suited to self-calibration. Most importantly, multi-scale survey designs could potentially become redundant, thus shortening flight time or increasing possible areal coverage.

Rain throughfall under vegetation is determined by characteristics of the vertical structure and the associated plant traits. It goes both ways: A protective layer of ground covering vegetation or leaf litter can decrease throughfall kinetic energy (TKE), whereas the formation of large drips in the canopy layers has been found to increase TKE. Abstracting the three-dimensional vegetation structure into usable quantitative metrics is challenging, and therefore these processes have not yet been sufficiently integrated into spatial erosion models. The vegetation splash factor (VSF) was designed to close this gap (Senn et al. 2020, DOI: 10.1002/esp.4820). The VSF quantifies the influence of vegetation on TKE and can be calculated from aerial lidar point clouds. In the first step, we derive the vegetation cover in a voxel space, which then allows modelling the proportional contribution of drips per layer to reach the ground. Hence, the approach is strictly based on the 3D structure rather than conventional forestry parameters, e.g. crown diameter or leaf sizes. Here, we present the result of the first application of the VSF in a small scale field study using splash cup measurements to validate and refine the concept.We implemented the experiment in a mixed-broadleaf forest near Bretten, Germany with a beech and an oak-dominated plot to cover a variety of vertical forest structure configurations and a diverse composition of species. Each plot comprised two transects of ten splash cups to measure sand loss - as a proxy for TKE - during six individual rainfall events. In addition, we used micro-scale runoff plots to determine the effect of soil covering layers such as leaf litter or biological soil crusts in comparison to bare soil. The VSF was calculated in R with a voxel resolution of 0.5 x 0.5 x 0.25 m using a UAV lidar dataset. Initial results from the splash cup measurements showed that young oak induced about 70 % higher TKE than adult beech trees. Among the individual cup positions, the lowest energy values were measured without canopy influence as freefall kinetic energy (FKE), TKE at positions with an intermediate young growth and shrub layer showed medium values. In near-trunk and mid-positions without intermediate layers, we measured TKE values more than twice as high as FKE. This resulted in significant sediment removal beneath the tree layer when the ground covering vegetation layer was removed, which is in accordance with studies from other ecosystems. Grouped according to these conventional vegetation structural criteria, we found that the calculated VSF values clustered around similar values and correlated with sand loss from splash cups. From these initial results, we assume general suitability of the VSF to reflect the influence of vegetation structure on TKE. Further, more detailed analysis will now be done to adjust and calibrate the VSF model to produce more indicative results. The preliminary findings presented here will be further expanded to be presented at vEGU21.

Soil loss caused by erosion has enormous economic and social impacts. Splash effects of rainfall are an important driver of erosion processes; however, effects of vegetation on splash erosion are still not fully understood. Splash erosion processes under vegetation are investigated by means of throughfall kinetic energy (TKE). Previous studies on TKE utilized a heterogeneous set of plant and canopy parameters to assess vegetation's influence on erosion by rain splash but remained on individual plant‐ or plot‐levels. In the present study we developed a method for the area‐wide estimation of the influence of vegetation on TKE using remote sensing methods. In a literature review we identified key vegetation variables influencing splash erosion and developed a conceptual model to describe the interaction of vegetation and raindrops. Our model considers both amplifying and protecting effect of vegetation layers according to their height above the ground and aggregates them into a new indicator: the Vegetation Splash Factor (VSF). It is based on the proportional contribution of drips per layer, which can be calculated via the vegetation cover profile from airborne LiDAR datasets. In a case study, we calculated the VSF using a LiDAR dataset for La Campana National Park in central Chile. The studied catchment comprises a heterogeneous mosaic of vegetation layer combinations and types and is hence well suited to test the approach. We calculated a VSF map showing the relation between vegetation structure and its expected influence on TKE. Mean VSF was 1.42, indicating amplifying overall effect of vegetation on TKE that was present in 81% of the area. Values below 1 indicating a protective effect were calculated for 19% of the area. For future work, we recommend refining the weighting factor by calibration to local conditions using field‐reference data and comparing the VSF with TKE field measurements. © 2020 The Authors. Earth Surface Processes and Landforms published by John Wiley & Sons Ltd, The Vegetation Splash Factor maps the amplification or reduction of rain‐fall kinetic energy based on the three‐dimensional vegetation structure. The presented approach allows the area‐wide application based on aerial lidar point clouds and can improve the representation of vegetation in erosion models. This study features a literature review and a case study documenting the development of the new approach., Deutsche Forschungsgemeinschaft http://dx.doi.org/10.13039/501100001659