Predicting bearing capacity and trafficability classes of forest roads using road properties and surrounding terrain information

Mills wish to maintain a year-round continuous flow of fresh roundwood, which is a major challenge during low-bearing capacity seasons in Finland, as there are few forest roads in the current network that are suitable for year-round use. This study explores methods to predict the bearing capacity of forest roads in Finland, using easily measurable road and terrain characteristics which can be potentially obtained through remote sensing. Traditional manual assessments of road conditions are labor-intensive, highlighting the need for alternative methods. The study found that road width is the most influential factor in predicting bearing capacity, with LiDAR data being potentially useful for measuring this variable. Despite moderate model results (R2 = 25−34%, RMSE% = 36−38%), the trafficability classification showed low accuracy, peaking at 38.7%. Gamma-ray data added limited value in predicting road capacity, indicating that its utility is minimal compared to other factors such as road width.