Estimating the Rut Depth by UAV Photogrammetry
AbstractThe 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
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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.
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 Sensing. 2017; 9(12):1279.Chicago/Turabian Style
Nevalainen, Paavo; Salmivaara, Aura; Ala-Ilomäki, Jari; Launiainen, Samuli; Hiedanpää, Juuso; Finér, Leena; Pahikkala, Tapio; Heikkonen, Jukka. 2017. "Estimating the Rut Depth by UAV Photogrammetry." Remote Sens. 9, no. 12: 1279.
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