In order to locate historical traces, drone-based Laserscanning has become increasingly popular in archaeological prospection and historical conflict landscapes research. The low resolution of aircraft-based Laserscanning is not suitable for small-scale detailed analysis so that high-resolution UAV-based LiDAR data are required. However, many of the existing studies lack a systematic approach to UAV-LiDAR data acquisition and point cloud filtering. We use this methodology to detect anthropogenic terrain anomalies. In this study, we systematically investigated different influencing factors on UAV-LiDAR data acquisition. The flight parameters speed and altitude above ground were systematically varied. In addition, different vegetation cover and seasonal acquisition times were compared, and we evaluated three different types of filter algorithms to separate ground from non-ground. It could be seen from our experiments that for the detection of subsurface anomalies in treeless open terrain, higher flight speeds like 6m/s were feasible. Regarding the flight altitude, we recommend an altitude of 50–75m above ground. At higher flight altitudes of 100–120m above ground, there is the risk that terrain characteristics smaller than 50cm will be missed. Areas covered with deciduous forest should only be surveyed during leaf-off season. In the presence of low-level vegetation (small bushes and shrubs with a height of up to 2m), it turned out that the morphological filter was the most suitable. In tree-covered areas with total absence of near ground vegetation, however, the choice of filter algorithm plays only a subordinate role, especially during winter where the resulting ground point densities have a percentage deviation of less than 6% from each other.

The transformation of Second World War heritage sites is a common challenge for today's memory culture. In this project, we combine ground truthing with drone-based, high-resolution laser scanning to document recent anthropogenically and environmentally caused transformation processes, and to raise public awareness of the importance of the ever-changing conflict landscape of the ‘Huertgen Forest’.

The documentation of historical remains and cultural heritage is of great importance to preserve historical knowledge. Many studies use low-resolution airplane-based laser scanning and manual interpretation for this purpose. In this study, a concept to automatically detect terrain anomalies in a historical conflict landscape using high-resolution UAV-LiDAR data was developed. We applied different ground filter algorithms and included a spline-based approximation step in order to improve the removal of low vegetation. Due to the absence of comprehensive labeled training data, a one-class support vector machine algorithm was used in an unsupervised manner in order to automatically detect the terrain anomalies. We applied our approach in a study site with different densities of low vegetation. The morphological ground filter was the most suitable when dense near-ground vegetation is present. However, with the use of the spline-based processing step, all filters used could be significantly improved in terms of the F1-score of the classification results. It increased by up to 42 percentage points in the area with dense low vegetation and by up to 14 percentage points in the area with sparse low vegetation. The completeness (recall) reached maximum values of 0.8 and 1.0, respectively, when taking into account the results leading to the highest F1score for each filter. Therefore, our concept can support on-site field prospection.

<p>In recent decades, due to the increasing weight of agricultural machineries, undesirable soil compaction has become a severe factor for soil degradation. It does not only have negative ecological, but also economic effects. Successfully detecting, monitoring and predicting depths of ruts that were used by heavy vehicles can potentially reduce soil compaction. A crucial task is to generate real-world data at site-specific locations. UAV-based approaches have the advantage of providing sufficient spatial coverage and resolution to assess vital information, which can be used to directly evaluate the compaction resulting from the utilization of these heavy machineries. This information could be used for agricultural predictions, optimized routing and best time for treatments, soil regeneration purposes and many more. Therefore, the aim of this research was to spatially detect the depth of ruts, caused by heavy farm machineries on agricultural fields with consumer-grade Unmanned Aerial Vehicles (UAVs).</p><p>We therefore created a semi-automatic processing pipeline for UAV based data. The georeferenced RGB orthomosaic was used to spatially predict lanes, in the early stages of the crop cycle, by employing a Machine Learning approach. This prediction was subsequently used to extract the height information of the rut and the surrounding area from the SfM (Structure from Motion) Digital Elevation Model. As a reference method for the absolute height information, we compared this DEM (DJI Phantom 4) to the UAV - LiDAR derived DEM (RIEGL miniVUX-1UAV). For both systems, no substantial difference in the quality of the evaluated compaction depth was observed. This allows the use of low-cost UAV RGB systems to contribute to the ongoing research on soil compaction.</p>