The Swedish forest industry is in the midst of a paradigm shift towards sustainable precision forestry. This work package contributes to this shift by analysing the need for, and the availability of, data for value creation,as well as developing different types of decision support. The goal of the research is to enable forest management and operations that take into account financial, ecological and social aspects.
For example through well-founded decisions on how the forest should be established and managed, how to minimise soil damage from off-road driving, increased foresight and resource efficiency through better control of thinning needs, and through models for assessing the sustainability of forest value chains. One important focus is to make the adoption of the models and decision support easier for many, for example through early involvement of stakeholders and by ensuring that the models are adapted for use with well-established decision support systems for forest planning.
The work package focuses on five sub-sectors:
Prototype of the new Heureka – adapted for high-resolution data
Usually, decisions about how to manage the forest are based on dividing the forest into larger homogeneous units. However, today there are various methods based on remote sensing that can provide us with detailed information about the forest at a much higher level of resolution. This can provide financial and ecological benefits as forest management can be adapted to the natural variation in the landscape, which means a step towards precision forestry. Unfortunately, no planning systems that fully utilise these new possibilities are currently available. Therefore this work package develops models and methods for decision support that fully utilise this kind of new high resolution data in forest planning. This is in order to enable a prototype for a new version of the forestry analysis and planning system, Heureka.
Digital planning of forestry operations
This sub-sector contributes to more sustainable forestry by reducing the fuel consumption of forestry machines, and the incidence of terrain damage during transportation. It develops advanced decision support for operational planning by combining data on terrain conditions and the geographical characteristics of the site, among other things. In this sub-sector, the researchers want to continue to develop a model that provides the forestry planner with a digital description of the thinning needs. They also want to develop an updated version of the decision support, GoForward, which optimises the forwarder’s work, and to investigate whether harvesting data can teach AI to make felling plans that can provide decision support for automated harvesting in the future.
Data resources for sustainable value creation
Mistra Digital Forest’s first phase provided a valuable arena for discussing which data sources are important for generating sustainable value in Swedish forestry. Work is now continuing to map these data sources in a structured way and to create a holistic overview. This includes identifying the availability and quality of the data sources. One particular focus is to investigate how the availability of high-quality data can contribute to the use of forest resources in a way that ensures financial, social and environmental values. The results will be disseminated nationally and internationally for research and praxis.
Artificial intelligence to achieve business goals
This sub-sector explores how artificial intelligence (AI) can be used to improve performance, profitability and sustainability in the forest industry. The focus is on applied, broad AI rather than cutting-edge solutions. So among other things, the researchers will identify and investigate the organisational challenges that might stand in the way of benefiting from AI. To address these challenges joint workshops focused on AI-driven problem solving will be conducted with key stakeholders in the forestry value chain, for example. This sub-sector contributes with practical implications for leadership and governance of applied AI at organisational and industry level.
Sustainability assessment of forest value chains
Here, methods and tools for making adequate sustainability assessments are developed. The assessments cover the entire value chain from raw material to finished product. In the first phase of the programme, methods were developed to provide an overview of how the aspects of climate, biodiversity and social and financial sustainability are affected by the choice of management strategy, for example. In this second phase, existing sustainability assessments are improved and further aspects of sustainability are added. Sustainability assessments should be simple to do, both for the individual actor and at the regional level. Therefore, the methods are integrated into existing tools such as BioMapp and Heureka. Central to this sub-sector is finding a broad consensus on the methods to be used to assess sustainability.