Modern forest management is increasingly tasked with simultaneously meeting economic, ecological, and social objectives. At the same time, forestry faces new opportunities to make harvesting decisions more efficient, due to recent advancements in technology and digital analytics. This new technology can be used to describe a forest in detail, down to individual tree level. With this information, harvesting decisions can be made at the same level of detail. In forestry terms, that type of management planning is called individual tree selection (ITS). By permitting trees to mature at different sizes at different times, depending on factors affecting the value-production of the individual tree, stand structures often develop a large variation of tree sizes. Such uneven-sized forests represent structures that are uncommon in traditional forestry today, while in many cases mimicking more natural dynamics. Therefore, ITS offers the prospect of enhanced ecological benefits and improved long-term sustainability while simultaneously maintaining profitability. This is possible to the extent that detailed information about the forest is available, and thorough research has been conducted to determine optimal timepoint of individual tree harvest.
The project is centered on developing decision-support tools tailored to ITS while balancing objectives of profitability and biodiversity. The focus is on the humid continental climate zones of Finland, Estonia, Latvia, and Sweden, where similar growing conditions prevail. The suggested plan for the project includes model development to improve growth simulation of the commercial species in the region. Two complementary optimization strategies will be applied. One strategy investigates the option of directly calculating which trees to remove in a specific stand. The other strategy, employs optimization through comprehensive system simulations of representative stands, to produce more generalized tree-selection rules. Biodiversity is integrated into the optimization by exploring and incorporating relevant variables.
By synthesizing high-resolution data with robust mathematical optimization and cross-disciplinary expertise, the project will, apart from investigating the potential of ITS, foster cross-country capacity building in the Nordic-Baltic region and promote sustainable management practices.
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