Forestry Engineer Veronica (Ruut) Mikkola works as an expert in the Future Bioeconomy research group at Lapland University of Applied Sciences.

In forest counselling, we encounter forest owners with very different levels of knowledge and varying forest management objectives. For this reason, developing different methods of forest advisory is extremely important. Visual modeling of forests provides an important tool for illustrating forest conditions and the required management actions. Through modeling, we can, for example, present the extent of different types of forest damage in a more comprehensible way for forest owners. The more visual the outcome, the easier it is for forest owners with different levels of expertise to understand the situation and grasp what is happening. Even experienced forest owners can gain a clearer overall picture of the condition of their forests.

In the ClimateForest project, two new tools are being developed to support forest counselling and risk management. A new approach is being pursued through a data processing platform called the Data Science Platform (Figure 1), into which researchers and forestry professionals can input collected research data. The platform generates clear charts from the results, which are easier to interpret than raw data or reference values. After entering the data, users can define what aspects they want to emphasize and what comparisons should be made. Once charts and diagrams are generated, the tool allows users to directly create publications in which these visuals are presented as part of the research results. Integrating visualization into the platform enhances the impact of outputs by creating a realistic, data-based representation of forests.

This platform makes it easier in advisory situations to illustrate the spread of forest damage to forest owners, as well as how diseases might develop in the coming years. When potential impacts of climate change are also considered, it becomes possible to provide an even clearer outlook on the future.

Figure 1. Front page of the Data Science Platform

To ensure clearer presentation, the project is also developing a solution that includes 3D visualization and VR technology. This enables the creation of different scenarios describing the current status of forest damage, its development, and its potential regional spread. The visualization is being developed so that forests can be examined at multiple scales: from large areas to stand level, and even down to individual trees (Figure 2).

If needed, for example, the occurrence of Scots pine blister rust in a tree can be easily shown visually to the forest owner, along with explanations about the disease and how trees become infected. This type of engaging presentation increases awareness and makes it easier for both forestry professionals and forest owners to monitor forest health.

Figure 2. Progress of 3D modeling at tree level

For modeling purposes, it has been essential to understand the characteristics of diseases and damage: how they appear in trees, how trees are affected, how severe the damage is, and how it impacts timber quality and value.

One of the causes of forest damage included in the project is Scots pine blister rust (Figure 3), one of the most common diseases affecting pine. It causes resinous growths on the trunk and branches as well as yellowing of the crown. Ultimately, the disease can lead to the death of the tree top or even the entire tree. Approximately 2–4% of pines may be affected, and in the worst cases, local infection rates can exceed 30%. The disease spreads through spores and can persist in trees for decades. (Luonnonvarakeskus 2026a.)

Figure 3. Crown dieback caused by Scots pine blister rust (Photo: Veronica Mikkola).

This disease spreads in pine forests in northern Sweden and Finland. Studies conducted by researchers involved in the project have shown connections between the disease and climate-related factors such as temperature, soil moisture, and soil nutrient levels. Climate warming is expected to further increase the spread of the disease. (Sveriges lantsbruksuniversitet 2026)

Another major cause of forest damage that was included in the project is the moose. In Finland and Sweden, the distribution of moose and the damage they cause covers the entire country. Moose are the most significant cause of damage to pine and deciduous saplings, and the amount of damage is directly linked to the size of the moose population. However, the occurrence and severity of damage are also influenced by regional variations in forest characteristics. (Luonnonvarakeskus 2026b). In Sweden, moose damage is more extensive than in Finland, and in some areas nearly one in five young pines has been damaged. The main contributing factor is the higher moose population, despite efforts to reduce it. (Metsälehti 2024)

Figure 4. Moose in a winter landscape (Photo: Sirja Sarkkinen).

Moose damage most often affects deciduous and pine saplings that are 1–3 meters tall, although smaller stands may also be affected (Figure 5). In summer, moose prefer easily digestible leaves and shoots of deciduous trees. In winter, they feed mainly on the tops and branches of pine trees, which provide the largest amount of available biomass. (Luonnonvarakeskus 2026b)

Figure 5. Moose damage in a pine-dominated young stand (Photo: Juha-Pekka Hotanen, Luke).

Using field-collected image data and 3D visualization techniques (Gaussian splatting), the project ensures that modelers have all the essential information needed for modeling diseases and damages. For modelers, it is crucial to understand how Scots pine blister rust manifests and what a moose-damaged stand looks like. This has been essential for producing realistic models and achieving the project’s objectives. Experts specializing in forest damage have also been involved in verifying and validating damage observations.

By combining forest data and modeling, it is possible to obtain a clear picture of forest health and how it can be maintained. If forest damage is detected, data and models can also be used to create guidelines on how to mitigate the spread of damage and what actions are required. Forecasting and anticipation are also very important aspects.

The goal of the ClimateForest project is to combine data, modeling, and the impacts of climate change into a single comprehensive framework. The tools developed in the project can be used to:

  • Illustrate damage risks on maps and visualize them as 3D and VR models
  • Plan measures to mitigate damage
  • Anticipate future developments and prepare in advance

When forest data is transformed into images, models, and immersive experiences, the condition of the forest becomes understandable in a completely new way. The aim of the ClimateForest project is to make this as easy as possible—for decision-makers, researchers, forestry professionals, and forest owners alike. As understanding increases, decision-making also improves. Together, we can anticipate changes, prepare for them, and ensure the well-being of forests in a changing climate.

ClimateForest project

Climate Change Adaptation of Northern Forests: Risks and Prevention of Damaging Agents

Duration: 1 March 2024 – 28 February 2027

Funding: Interreg Aurora (co-funded by the EU), Regional Council of Lapland

Partners: Lapland UAS (Lead partner), Luleå University of Technology (LTU), Swedish Forest Agency, Swedish University of Agricultural Sciences (SLU), Natural Resources Institute Finland (Luke), Finnish Forest Centre

Sources

Luonnonvarakeskus, 2026. Metsätuhon aiheuttaja. Tervasroso. Viitattu 22.1.2026 Tervasroso | Luonnonvarakeskus

Luonnonvarakeskus, 2026. Metsätuhon aiheuttaja. selkärankaiset. Viitattu 22.1.2026 Hirvi | Luonnonvarakeskus

Metsälehti, 2024. Ruotsin hirvivahingot yhä korkealla tasolla – paikoin lähes viidennes nuorista männyistä vahingoittunut. Viitattu 4.5.2026 Ruotsin hirvivahingot yhä korkealla tasolla – paikoin lähes viidennes nuorista männyistä vahingoittunut – Metsälehti

Sveriges lantsbruksuniversitet, 2026. Törskate ökar i norr – här är faktorer som är förknippade med risken. Viitattu 4.5.2026 Törskate ökar i norr – här är faktorer som är förknippade med risken | slu.se