To investigate relevant processes as well as to predict the possible impact of soil erosion, many soil erosion modelling tools have been developed. The most productive development of process-based models took place at the end of the 20th century. Since then, the methods available to observe and measure soil erosion features as well as methods to inter- and extrapolate such data have undergone rapid development, e.g., photogrammetry, light detection and ranging (LiDAR) and sediment tracing are now readily available methods, which can be applied by a broader community with lower effort. This review takes 13 process-based soil erosion models and different assessment techniques into account. It shows where and how such methods were already implemented in soil erosion modelling approaches. Several areas were found in which the models miss the capability to fully implement the information, which can be drawn from the now-available observation and data preparation methods. So far, most process-based models are not capable of implementing cross-scale erosional processes and can only in parts profit from the available resolution on a temporal and spatial scale. We conclude that the models’ process description, adaptability to scale, parameterization, and calibration need further development. The main challenge is to enhance the models, so they are able to simulate soil erosion processes as complex as they need to be. Thanks to the progress made in data acquisition techniques, achieving this aim is closer than ever, if models are able to reap the benefit.

Dead wood such as coarse dead wood debris (CWD) is an important component in natural forests since it increases the diversity of plants, fungi, and animals. It serves as habitat, provides nutrients and is conducive to forest regeneration, ecosystem stabilization and soil protection. In commercially operated forests, dead wood is often unwanted as it can act as an originator of calamities. Accordingly, efficient CWD monitoring approaches are needed. However, due to the small size of CWD objects satellite data-based approaches cannot be used to gather the needed information and conventional ground-based methods are expensive. Unmanned aerial systems (UAS) are becoming increasingly important in the forestry sector since structural and spectral features of forest stands can be extracted from the high geometric resolution data they produce. As such, they have great potential in supporting regular forest monitoring and inventory. Consequently, the potential of UAS imagery to map CWD is investigated in this study. The study area is located in the center of the Hainich National Park (HNP) in the federal state of Thuringia, Germany. The HNP features natural and unmanaged forest comprising deciduous tree species such as Fagus sylvatica (beech), Fraxinus excelsior (ash), Acer pseudoplatanus (sycamore maple), and Carpinus betulus (hornbeam). The flight campaign was controlled from the Hainich eddy covariance flux tower located at the Eastern edge of the test site. Red-green-blue (RGB) image data were captured in March 2019 during leaf-off conditions using off-the-shelf hardware. Agisoft Metashape Pro was used for the delineation of a three-dimensional (3D) point cloud, which formed the basis for creating a canopy-free RGB orthomosaic and mapping CWD. As heavily decomposed CWD hardly stands out from the ground due to its low height, it might not be detectable by means of 3D geometric information. For this reason, solely RGB data were used for the classification of CWD. The mapping task was accomplished using a line extraction approach developed within the object-based image analysis (OBIA) software eCognition. The achieved CWD detection accuracy can compete with results of studies utilizing high-density airborne light detection and ranging (LiDAR)-based point clouds. Out of 180 CWD objects, 135 objects were successfully delineated while 76 false alarms occurred. Although the developed OBIA approach only utilizes spectral information, it is important to understand that the 3D information extracted from our UAS data is a key requirement for successful CWD mapping as it provides the foundation for the canopy-free orthomosaic created in an earlier step. We conclude that UAS imagery is an alternative to laser data in particular if rapid update and quick response is required. We conclude that UAS imagery is an alternative to laser data for CWD mapping, especially when a rapid response and quick reaction, e.g., after a storm event, is required.

High-resolution information on the processes and rates of soil erosion, transport, and deposition, offer important knowledge for soil erosion modelling, and the protection and sustainable management of soil. It helps improve the cross-scale understanding on aspects as aggregate breakdown, rill erosion, swelling and shrinking effects, and rill-network evolution. As a non-invasive, high-resolution, and cost as well as time-efficient method, Structure from Motion (SfM) presents a valuable tool to calculate soil loss, depict soil surface change detection, and offer high-resolution information on soil and soil erosion processes. Even though SfM shows in general higher erosion rates, due to the influence of non-erosive processes, the technique is altogether in good agreement with the sampling data at the outlet. We monitor soil erosion on multiple erosional plots and with spatial and temporal high-resolution photogrammetry to assess its feasibility over time.For this purpose, we conduct 12 rainfall simulations on a three times one metre plot, on different sides, with different vegetation cover, tillage, and initial soil conditions. Seven to ten synchronized time-lapse cameras are set up around the plot, taking pictures every 10-60 seconds. The data thus obtained allow change detection assessment via digital elevation models of difference at least once per minute. The elevation change by SfM is validated via bulk density measurements, and sampling at the plot’s outlet assessing runoff, and sediment concentration at minute intervals. During an overflow experiment, we measure flow velocity via video using particle tracer and manually via colour tracer, gaining spatial and temporal distribution information on the flow velocity. Using low-cost sensors, we furthermore monitor the progress of the soil moisture and temperature during the whole rainfall simulation.We present sampled and photogrammetric results based on a dozen rainfall simulations at the micro-scale with a very high temporal and spatial resolution. This gives an insight into spatial distribution and development of soil erosion processes on a sub-minute resolution. We compare these data to gain knowledge on the feasibility of temporal and spatial high-resolution SfM soil erosion assessment and their usability for the validation and calibration of process-based soil erosion models.

Over the past years studies (e.g., Hänsel et al. 2016 or Yang et al. 2021) have shown the feasibility of camera-based soil erosion assessment. This cost-efficient and non-invasive photogrammetric approach is a valuable tool to meassure soil surface changes (Balaguer-Puig et al., 2018). A challenging aspect nevertheless represents the masking of the sediment yield by surface lowering processes such as soil consolidation and compaction (Ehrhardt et al. 2022). Based on the camera elevation changes and measured field observations, we developed an approach to estimate these masking effects in the beginning of rainfall events and approximate a correction function.We conducted ten rainfall simulations at plots with 3 m length and 1 m width on agricultural slopes. The runoff and sediment concentration were measured at the plots outlet, while a time-lapse camera system surrounding the plot took images every few seconds. We furthermore collected data on soil bulk density, soil moisture, grain size distribution, total organic carbon, slope steepness, surface cover and surface roughness. To describe the changes of the soil surface at the beginning of the rainfall events, dominated by the masking effects, S-shaped curves were fitted via non-linear regression for each rainfall experiment. We then used the variables of those functions as well as the field observations as input values for an adjustment to estimate masking effects at the beginning of rainfall simulations as functions of soil and plot characteristics.The best results were achieved using four observations: grain size distribution, slope, bulk density and total carbon content. Our approach shows the potential to disentangle soil surface changes due to erosion and non-erosion processes at the onset of rainfall events. While the model worked well for most of the rainfall simulations, predictions were challenging for those events with strongly varying field observations. Especially difficult were those simulations conducted on freshly tilled soils. They showed high elevation changes at the beginning of the event that had great potential for soil consolidation and thus the mixed signals regarding the different processes were not separable by our approach. Nevertheless our study showed potential to increase the informative value of camera-based soil erosion measurements on agricultural fields. References Balaguer-Puig, M.; Marqués-Mateu, Á.; Lerma, J.L.; Ibáñez-Asensio, S. Quantifying small-magnitude soil erosion: Geomorphic change detection at plot scale. Land Degrad Dev 2018, 29, 825-834, doi:10.1002/ldr.2826.Ehrhardt, A.; Deumlich, D.; Gerke, H.H. Soil Surface Micro-Topography by Structure-from-Motion Photogrammetry for Monitoring Density and Erosion Dynamics. Front. Environ. Sci. 2022, 9, doi:10.3389/fenvs.2021.737702.Hänsel, P.; Schindewolf, M.; Eltner, A.; Kaiser, A.; Schmidt, J. Feasibility of High-Resolution Soil Erosion Measurements by Means of Rainfall Simulations and SfM Photogrammetry. Hydrol 2016, 3, 38, doi:10.3390/hydrology3040038.Yang, Y.; Shi, Y.; Liang, X.; Huang, T.; Fu, S.; Liu, B. Evaluation of structure from motion (SfM) photogrammetry on the measurement of rill and interrill erosion in a typical loess. Geomorphology 2021, 385, 107734, doi:10.1016/j.geomorph.2021.107734.