ABSTRACT

Even at a well-managed harvesting site, vehicle trafficking occurs on at least 12% of the area and might cause ruts and compaction. The use of drones for inventory and mapping in forestry is still a new method. The purpose of this study was to develop a method for measuring the size and depth of wheel ruts caused by forest machines in harvested areas, using drones and Structure from Motion photogrammetry. In order to investigate the accuracy of drone photogrammetry, measurements from flight altitudes of 60 m and 120 m above ground level were compared with manual measurements. The same methods were used at a control site on farm land, taking into account the rut depth and the location of the sample surface (close to trees or in a fully open area). No statistically significant differences were found between manual measurements and remote measurements from 60 m or 120 m altitude at the harvesting site (R² 0.77–0.83). At the control site, an underestimation of 2.2 cm of the rut depth was found for remote measurements made from 120 m altitude. The data derived from drone images were able to reproduce the 3D model of surface features, such as bulges and ruts; these measurements were considered to be equivalent to manual measurements. For practical applications, a post-harvest survey using drones could contribute to verifying compliance with international forest certification standards or by private contractors to evaluate rut formation on their harvest sites.

摘要

即使在管理良好的采伐场，至少有 12% 的区域会发生车辆贩运，并可能导致车辙和压实。在林业中使用无人机进行清点和测绘仍然是一种新方法。本研究的目的是开发一种方法，使用无人机和动态摄影测量中的结构来测量森林机械在采伐区造成的车轮车辙的大小和深度。为了研究无人机摄影测量的准确性，将来自地面以上 60 m 和 120 m 飞行高度的测量值与手动测量值进行了比较。考虑到车辙深度和样品表面的位置（靠近树木或完全开放的区域），在农田的控制地点使用了相同的方法。² 0.77–0.83）。在控制地点，从 120 m 高度进行的远程测量发现车辙深度被低估了 2.2 cm。来自无人机图像的数据能够再现表面特征的 3D 模型，例如凸起和车辙；这些测量被认为等同于手动测量。在实际应用中，使用无人机进行的收获后调查有助于验证是否符合国际森林认证标准或由私人承包商评估其收获地点的车辙形成情况。