Solifluction is one of the most widespread periglacial processes with low annual movement rates in the range of —millimeters to centimeters. Traditional methods to assess solifluction movement usually have low spatial resolution, which hampers our understanding of spatial movement patterns and the factors controlling them. In this study, we (a) test the applicability of unmanned aerial vehicle (UAV)‐based structure‐from‐motion photogrammetry in comparison to a traditional total station survey to map surface movement of a turf‐banked solifluction lobe (TBL) in the Turtmann Valley (Switzerland). We then (b) relate the detected movement patterns to potential geomorphometric, material, thermal and vegetation controls, which we assessed using geomorphic and vegetation mapping, electrical resistivity surveys and temperature loggers. Our results show that (a) UAV‐based mapping can detect solifluction movement with high spatial resolution (one point per m2, total > 900 points) and rates and patterns consistent with a total station survey, but requires careful measurement set‐up and analysis; and (b) movement rates differ between lobe tread, riser and a ridge feature. Differences can be explained by heterogeneous material, geomorphometric, thermal and vegetation properties of the TBL, which promote different solifluction processes. Our study demonstrates the applicability of UAV‐based mapping in solifluction research and improves our understanding of solifluction processes and landform development.

Abstract. UAS imagery has become a widely used source of information in geomorphic research. When photogrammetric methods are applied to quantify geomorphic change, camera calibration is essential to ensure accuracy of the image measurements. Insufficient self-calibration based on survey data can induce systematic errors that can cause DEM deformations. The typically low geometric stability of consumer grade sensors necessitates in-situ calibration, as the reliability of a lab based calibration can be affected by transport. In this research a robust on-site workflow is proposed that allows the time-efficient and repeatable calibration of thermal and optical sensors at the same time. A stone building was utilised as calibration object with TLS scans for reference. The approach was applied to calculate eight separate camera calibrations using two sensors (DJI Phantom 4 Pro and Workswell WIRIS pro), two software solutions (Vision Measurement System (VMS) and Agisoft Metashape) and two different subsets of images per sensor. The presented results demonstrate that the approach is suitable to determine camera parameters for pre-calibrating photogrammetric surveys.

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