Information on timber assortment recovery and wood quality is crucial for wood procurement planning, as the various tree species and wood dimensions and qualities may be utilized and refined in separate mills. The aim of this thesis is to improve our understanding of the timber trade in digital environments in order to support the planning of harvesting operations.
The work for the thesis was carried out in three areas, two of which (discussed in Papers I and II) were located in Eastern Finland and one (Paper III) in Southern Finland. The field data comprised tree characteristics obtained from 79, 99 and 665 sample plots (Papers I, II and III, respectively), 249 harvested stands (Paper III) and a stem quality database (Papers I and III), whereas the remote sensing material consisted of aerial imagery (Papers I and III) and airborne laser scanning (ALS) data (Papers I, II and III) covering all the sites.
With the stated overarching aim, we set out in Papers I and III to estimate timber assortment volumes, economic values and wood paying capabilities (WPC) for plots (Paper I) or stands (Paper III) with different bucking scenarios, and used the resulting timber assortment estimates to assess logging recoveries. The alternative bucking scenarios investigated were (1) bucking-to-value using maximum sawlog and pulpwood volumes but excluding quality (theoretical maximum), and (2) bucking-to-value using sawlog lengths at 30 cm intervals for Scots pine (Pinus sylvestris L., Papers I and III) and Norway spruce (Picea abies (L.) H.Karst, Paper III) and veneer logs of lengths 4.7 m, 5.0 m, 6.0 m and 6.7 m for birch (Betula spp., Paper III), either excluding or including wood quality indicators. The first approach resembled the state-of-the-art in Nordic forestry business circles and the second approach went beyond that. The commercial value of timber stands is substantially affected by the quantity of understorey trees, and pre-harvest clearing is typically needed when forest stands have an understorey vegetation that hinders harvesting operations. We therefore proposed a method in Paper II for estimating this need for the pre-harvest clearing of small trees (diameters at breast height < 7 cm).
The results showed that use of the methods developed in this thesis could support wood procurement practices by (1) locating valuable stands with the desired timber assortment distributions (Papers I and III), (2) identifying understorey vegetation that needs to be removed before harvesting (Paper II), and (3) reducing costs, as the number of field visits needed before harvesting will diminish (Papers I, II and III).
In conclusion, the present findings may make timber markets more competent, since the methods developed here provide detailed pre-harvest information that can be used as a decision support tool by either buyers or sellers of timber in traditional and digital marketplaces.