The forest biomass supply represents an important part of the value chain for different wood-based products, and its environmental impacts are also frequently crucial. The performance of biomass supply chains (BSCs) can be assessed for various purposes and using a variety of methodological approaches, either including or excluding spatial properties. The purpose of this thesis was to investigate what kind of spatial data are required and available for case-specific BSC analyses in Finland, and what would be suitable levels of spatial precision for the various approaches. This thesis consists of five papers, one of which reviews case studies carried out in various geographical BSC environments around the world, while the remaining four are spatial case studies of BSC systems in Finland, three of them focusing on bioenergy production and one assessing the performance of a novel pulpwood transportation concept. A geographical information system (GIS) was used as the principal tool in one study, while in the other three the role of GIS was to produce spatially analysed data for life-cycle assessment and agent-based simulation. The main conclusion is that a spatial precision of between 1 km and 10 km, where each point of origin represents roughly an area of 1–100 km2, is sufficient for forest biomass data in Finnish BSC systems. The final precision should be determined collectively by the setup of the case study, factors leading to complexity in the supply chain system and the geographical extent of the area concerned. Relative to many other parts of the world, Finland has a readily available high quality source of spatial data for BSC research. It is recommended that GIS-based research could be improved by adding dynamic properties and stochasticity to the models, because temporal variations in feedstock supply and demand will probably increase in the future.
Forest growth information is important, and it is an issue of concern for various stakeholders. Forest growth has been traditionally assessed by way of modeling, however models fitted for a larger area tend to be biased when applied to local settings, and there is thus a crucial need for localization or other ways to improve the growth prediction of such models. There are various ways to achieve this improvement, one of which is by introducing new data elements. Consequently, this presented research used the known effect of topography on soil moisture content to achieve growth prediction improvements for local forest growth. A total of 9987 tally trees and 1118 sample trees distributed in 197 plots were used to examine the suitability of using terrain attributes derived from digital terrain model (DTM), airborne γ-ray and leaf area index (LAI) data, in improving pre-existing diameter at breast height (dbh) model for a five-year period in southeastern Finland. Statistical examinations of the mixed effect modeling (linear and non-linear) and multilayer perceptron modeling were used in the analysis. The results varied between sample trees and species. The best root mean square error (RMSE) improvements of the national model were obtained for broadleaved trees, followed by pine and spruce. All of the γ-ray windows were shown to be significant in eliminating the local bias. The effect of the DTM source showed that a higher resolution with a lower focal neighborhood is the best combination for quantifying terrain attributes, notably the Topographic Wetness Index (TWI). The growth prediction improvement tended to be more accurate in less fertile site types. LAI demonstrated improvement when combined with terrain attributes, especially intensity-based LAI. The presented study concludes that the newly introduced elements are suitable for improving local forest growth prediction with RMSE improvement between 6-18%.