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Launiainen, S., Ala-Ilomäki, J., Hiedanpää, J., Salmivaara, A., Finér, L., Nevalainen, P., Heikkonen, J., & Pahikkala, T. (2017). Estimating the Rut Depth by UAV Photogrammetry. Remote Sensing, 9, 1279.
https://doi.org/10.3390/rs9121279

has been cited by the following article:

• TITLE: Soil Trafficability Forecasting

  AUTHORS: Marie-France Jones, Paul Arp

  KEYWORDS: Soil Trafficability, Wood Forwarding, Plot Surveys, Regression Comparisons, Cartographic Depth-to-Water

  JOURNAL NAME: Open Journal of Forestry, Vol.9 No.4, September 29, 2019

  ABSTRACT: This article introduces and evaluates a Soil Trafficability Model (STRAM) designed to estimate and forecast potential rutting depth on forest soils due to heavy machine traffic. This approach was developed within the wood-forwarding context of four harvest blocks in Northern and Central New Brunswick. Field measurements used for model calibration involved determining soil rut depths, volumetric moisture content, bulk density, soil resistance to cone penetration (referred to as cone index, or CI), and the dimensionless nominal soil cone index (NCI) defined by the ratio of CI over wheel foot print pressure. With STRAM, rut depth is inferred from: 1) machine dimensions pertaining to estimating foot print area and pressure; 2) pore-filled soil moisture content and related CI projections guided by year-round daily weather records using the Forest Hydrology Model (ForHyM); 3) accounting for within-block soil property variations using multiple and Random Forest regression techniques. Subsequent evaluations of projected soil moisture, CI and rut-depth values accounted for about 40 (multiple regression) and 80 (Random Forest) percent of the corresponding field measured values.