Assessing Rut Depth Formation in a Skidding Operation Using UAV Photogrammetry
Abstract
Mechanical harvesting equipment used in forestry operations increases productivity; however, they may cause significant physical impacts on the soil. Among the most noticeable of these effects are wheel ruts caused by logging vehicles. Such effects can disrupt the physical structure of the soil, increase the risk of erosion, and damage forest ecosystems. In this study, wheel rut depths formed during ground-based skidding operations were analyzed comparatively using both a manual measurement method and a 3D image-based method implemented via Unmanned Aerial Vehicles (UAVs). The fieldwork was conducted during a skidding operation in the Dağtekke Forest Enterprise Chief in the city of İzmir. Manual measurements were performed using a ruler and a lath along skidding trails. For image-based method, aerial images of the study area were captured using UAV, and then used to generate Digital Surface Model (DSM). The results obtained from both methods were compared using correlation analysis and paired sample t-tests. The findings showed a high correlation between manual method and UAV-assisted method, indicating that UAV photogrammetry offers a fast and reliable alternative for rut depth monitoring. The UAV-derived measurements of rut depth showed a high level of accuracy, achieving a Mean Absolute Percentage Error (MAPE) of 16.86%. This study presents a novel application of UAV-based method for high-resolution, remote measurement of rut depth. It offers a cost-effective alternative to traditional methods while providing valuable insights into assessing the environmental impacts of mechanical equipment in forestry operations.
Keywords
Environmental impact, soil disturbance, rut depth, remote sensing, UAV photogrammetry
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