earticle

영어

Mechanized timber harvesting operations often cause soil disturbance, such as compaction and rutting. The extent of soil disturbance is highly dependent on environmental factors and operation methods. As soil disturbance has a long-term impact on forest productivity, it is critical to assess its effects. The objective of this study was to investigate soil surface deformation caused by forest machinery traffic in a steep slope clear-cut area and to compare manual methods with remote sensing. Following the timber harvesting operation design, we established the experimental treatments representing the number and direction of forest machinery passes (1D, 1-downward; 1U, 1-upward; 3R, 3-round-trip; 5R, 5-round-trip). Soil rut depth and cross-section were manually measured using pinboard and estimated by mobile LiDAR system (MLS) and unmanned aerial vehicle structure from motion algorithm (UAV SfM). There was a significant difference in soil rut depth based on the number of passes (p = 0.00), while no significant difference based on the direction of passes. Rut depth in 1D (22.2cm) was significantly higher than 3R (15.7cm), with no significant differences among 1D, 1U (20.0cm), and 5R (19.6cm) or among 3R, 1U, and 5R. These findings suggest that most soil disturbance occurs during the initial passes of forest machinery. The comparison of pinboard and MLS data revealed a significant relationship (R2 = 0.74, slope = 1.00, p = 0.00). Comparing pinboard and UAV SfM data, we found that MLS is more accurate than UAV SfM in assessing soil surface deformation (R2 = 0.60, slope = 0.81, p = 0.00). The results reveal the need to establish optimized driving routes for forest machinery to minimize soil disturbance and suggest the potential of using MLS and UAV SfM for future soil disturbance assessments.