Research Group Environmental Informatics and Municipal Planning, Institute of Computer Science, Osnabrück University, 49074 Osnabrück, Germany.
Remote Sensing Group, Institute of Computer Science, Osnabrück University, 49074 Osnabrück, Germany.
Sensors (Basel). 2021 Dec 29;22(1):217. doi: 10.3390/s22010217.
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 6 m/s were feasible. Regarding the flight altitude, we recommend an altitude of 50-75 m above ground. At higher flight altitudes of 100-120 m above ground, there is the risk that terrain characteristics smaller than 50 cm 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 2 m), 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.
为了定位历史痕迹，基于无人机的激光扫描在考古勘探和历史冲突景观研究中变得越来越流行。机载激光扫描的低分辨率不适合小规模详细分析，因此需要高分辨率的无人机激光雷达数据。然而，许多现有的研究缺乏系统的无人机激光雷达数据采集和点云滤波方法。我们使用这种方法来检测人为的地形异常。在这项研究中，我们系统地研究了不同的影响因素对无人机激光雷达数据采集的影响。系统地改变了飞行速度和离地高度等飞行参数。此外，还比较了不同的植被覆盖和季节性采集时间，并评估了三种不同类型的滤波算法来分离地面和非地面。从我们的实验中可以看出，对于无树木开阔地形中地下异常的检测，6 米/秒等较高的飞行速度是可行的。关于飞行高度，我们建议离地 50-75 米。在离地 100-120 米的较高飞行高度，可能会错过小于 50 厘米的地形特征。覆盖落叶林的区域应仅在落叶季节进行调查。在低植被（高度不超过 2 米的小灌木和灌木丛）存在的情况下，形态学滤波器是最合适的。然而，在树木覆盖的区域，完全没有近地植被，滤波器算法的选择只起次要作用，尤其是在冬季，地面点密度的差异百分比小于 6%。
