Current issue: 55(2)
Two different pulse density airborne laser scanning datasets were used to develop a quality assessment methodology to determine how airborne laser scanning derived variables with the use of reference surface can determine forest road quality. The concept of a reference DEM (Digital Elevation Model) was used to guarantee locally invariant topographic analysis of road roughness. Structural condition, surface wear and flatness were assessed at two test sites in Eastern Finland, calculating surface indices with and without the reference DEM. The high pulse density dataset (12 pulses m–2) gave better classification results (77% accuracy of the correctly classified road sections) than the low pulse density dataset (1 pulse m–2). The use of a reference DEM increased the precision of the road quality classification with the low pulse density dataset when the classification was performed in two-steps. Four interpolation techniques (Inverse Weighted Distance, Kriging, Natural Neighbour and Spline) were compared, and spline interpolation provided the best classification. The work shows that applying a spline reference DEM it is possible to identify 66% of the poor quality road sections and 78% of the good ones. Locating these roads is essential for road maintenance.
Detailed pre-harvest information about the volumes and properties of growing stocks is needed for increased precision in wood procurement planning for just-in-time wood deliveries by cut-to-length (CTL) harvesters. In the study, the non-parametric Most Similar Neighbour (MSN) methodology was evaluated for predicting external quality of Scots pine and Norway spruce, expressed as stem sections fulfilling the saw log dimension and quality requirements of Finnish forest industry, as they affect the recovery of timber assortments and the value of a pre-harvest stand. Effects of external tree quality were evaluated using saw log recovery and saw log reduction caused by stem defects, as well as total timber value (€) and average unit value (€ m–3) in a stand. Root mean square error (RMSE) of saw log recovery and reduction were 9.12 percentile points (pp) for Scots pine and 6.38 pp for Norway spruce stands. In the unit value considerations, the predictions compared with measurements resulted in the RMSE of 3.50 € m–3 and the bias of 0.58 € m–3 in Scots pine stands and 2.60 € m–3, and 0.35 € m–3 in Norway spruce stands, respectively. The presented MSN based approach together with the utilization of the external stem quality database included in the ARVO software could provide dimension and external quality predictions usable for pre-harvest assessment of timber stock at a stand level. This prediction methodology is usable especially in analyses where timber assortment recoveries, values and unit prices are compared when different bucking objectives are used.
In a closed market, roundwood buyers pricing system affect the roundwood flow from the stands to different roundwood users. If a buyer is capable to discriminate higher value stands from low quality stands better than its competitors, the buyer should be able to buy better raw material. In the study, a discrete event simulation was used to examine the effect of residual value appraisal (RVA) -based pricing of roundwood by log dimensions and grades compared to the traditional pricing based on average unit prices (UP) of roundwood assortments on roundwood flow. The core of the simulation model was the data containing 51 pine dominated stands from southern Finland. Sample trees were theoretically bucked by the bucking simulator in order to estimate the volumes, dimensions and grades of the logs and roundwood assortments. The simulation model of roundwood markets included four roundwood buyers, two corporations and two saw milling enterprises. The main finding of the study was that the buyers who use RVA gains an advantage and receives better quality compared to buyers who use UP. As the number of buyers using RVA increases, the competition increased and the advantage decreased.