Automated, adaptive methods for forest inventory

Forest inventories are used to estimate forest characteristics and the condition of forest for many different applications: operational tree logging for forest industry, forest health state estimation, carbon balance estimation, land-cover and land use analysis in order to avoid forest degradation etc. Recent inventory methods are strongly based on remote sensing data combined with field sample measurements, which are used to define estimates covering the whole area of interest. Remote sensing data from satellites, aerial photographs or aerial laser scannings are used, depending on the scale of inventory. To be applicable in operational use, forest inventory methods need to be easily adjusted to local conditions of the study area at hand. All the data handling and parameter tuning should be objective and automated as much as possible. The methods also need to be robust when applied to different forest types. Since there generally are no extensive direct physical models connecting the remote sensing data from different sources to the forest parameters that are estimated, mathematical estimation models are of "black-box" type, connecting the independent auxiliary data to dependent response data with linear or nonlinear arbitrary models. To avoid redundant complexity and over-fitting of the model, which is based on up to hundreds of possibly collinear variables extracted from the auxiliary data, variable selection is needed. To connect the auxiliary data to the inventory parameters that are estimated, field work must be performed. In larger study areas with dense forests, field work is expensive, and should therefore be minimized. To get cost-efficient inventories, field work could partly be replaced with information from formerly measured sites, databases. The work in this thesis is devoted to the development of automated, adaptive computation methods for aerial forest inventory. The mathematical model parameter definition steps are automated, and the cost-efficiency is improved by setting up a procedure that utilizes databases in the estimation of new area characteristics.

Forest Biomass Resources and Technological Prospects for the Production of Second-generation Biofuels in Finland by 2020

Introduction of second-generation biofuels is an essential factor for meeting the EU’s 2020 targets for renewable energy in the transport sector and enabling the more ambitious targets for 2030. Finland’s forest industry is strongly involved in the development and commercialising of second-generation biofuel production technologies. The goal of this paper is to provide a quantified insight into Finnish prospects for reaching the 2020 national renewable energy targets and concurrently becoming a large-scale producer of forest biomass based second-generation biofuels feeding the increasing demand in European markets. The focus of the paper is on assessing the potential for utilising forest biomass for liquid biofuels up to 2020. In addition, technological issues related to the production of second-generation biofuels were reviewed. Finland has good opportunities to realise a scenario to meet 2020 renewable energy targets and for large-scale production of wood based biofuels. In 2020, biofuel production from domestic forest biomass in Finland may reach nearly a million ton (40 PJ). With the existing biofuel production capacity (20 PJ/yr) and national biofuel consumption target (25 PJ) taken into account, the potential net export of biofuels from Finland in 2020 would be 35 PJ, corresponding to 2–3% of European demand. Commercialisation of second-generation biofuel production technologies, high utilisation of the sustainable harvesting potential of Finnish forest biomass, and allocation of a significant proportion of the pulpwood harvesting potential for energy purposes are prerequisites for this scenario. Large-scale import of raw biomass would enable remarkably greater biofuel production than is described in this paper.

CSR standards and financial performance in forest industry - Empirical analysis

The definition of corporate social responsibility (CSR) has been developed since 1950s but even today there is no consensus what CSR includes. The main purpose of this thesis was to find out whether financial performance is better among first adopters of CSR standards in forest industry. To support the main purpose it was critical also investigate what kind of companies adopt CSR standards. The empirical part of the thesis based on a survey which was done in 2010 to forest industry companies and financial data that was gathered from different databases from years 2003-2010. According to the research results it seems the early CSR standards adopters benefits the position of the first adopter many times. Especially cash position and solvency of early adopter companies were better than later adopters or those who did not adopt CSR standards at all. Profitability seemed to be better among CSR standards adopters but early adopters did not have significantly better position compared to later adopters. CSR standards adopters were companies that considered themselves as environmental performance pioneers and had employee oriented management.