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Open AccessArticle

Estimating the Rut Depth by UAV Photogrammetry

Department of Information Technology, University of Turku, FI-20014 Turku, Finland
Natural Resources Institute Finland (Luke), Latokartanonkaari 9, FI-00790 Helsinki, Finland
Metsälinkki Ltd., Kylämetsäntie 2 B, FI-28760 Pori, Finland
Author to whom correspondence should be addressed.
Remote Sens. 2017, 9(12), 1279;
Received: 24 September 2017 / Revised: 22 November 2017 / Accepted: 6 December 2017 / Published: 9 December 2017
The rut formation during forest operations is an undesirable phenomenon. A methodology is being proposed to measure the rut depth distribution of a logging site by photogrammetric point clouds produced by unmanned aerial vehicles (UAV). The methodology includes five processing steps that aim at reducing the noise from the surrounding trees and undergrowth for identifying the trails. A canopy height model is produced to focus the point cloud on the open pathway around the forest machine trail. A triangularized ground model is formed by a point cloud filtering method. The ground model is vectorized using the histogram of directed curvatures (HOC) method to produce an overall ground visualization. Finally, a manual selection of the trails leads to an automated rut depth profile analysis. The bivariate correlation (Pearson’s r) between rut depths measured manually and by UAV photogrammetry is r = 0.67 . The two-class accuracy a of detecting the rut depth exceeding 20 cm is a = 0.65 . There is potential for enabling automated large-scale evaluation of the forestry areas by using autonomous drones and the process described. View Full-Text
Keywords: micro-topography; forest harvesting; UAV; photogrammetry; micro-topography; point cloud; TIN; curvature; rut formation micro-topography; forest harvesting; UAV; photogrammetry; micro-topography; point cloud; TIN; curvature; rut formation
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Nevalainen, P.; Salmivaara, A.; Ala-Ilomäki, J.; Launiainen, S.; Hiedanpää, J.; Finér, L.; Pahikkala, T.; Heikkonen, J. Estimating the Rut Depth by UAV Photogrammetry. Remote Sens. 2017, 9, 1279.

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