WHFF Project 2019.09
Projektleitung: Leo Gallus Bont
The most important facts in brief
For many ecosystem services, there is still no adequate valorization at the operational level, which is why their participation in operational success can only be taken into account indirectly. In order to include these aspects in forest planning, suitable concepts and instruments for decision support are needed.
A decision support system (DSS) allows a differentiated evaluation and presentation of the influence of different treatment strategies on the sustainability of forest management under economic, ecological and social aspects.
The aim of this project was to adapt the DSS model and to complement it with a spatial optimization algorithm so that it automatically determines the ideal mix of different management strategies.
Among the management strategies considered, it was striking how well the scenarios without clearing (no management, plentering/permanent forest), performed in comparison to the classical management strategies in high forests.
This resulted in a trade-off between timber production and the indicator groups recreation, biodiversity and carbon storage, i.e. in a strategy where timber production performed well, the other indicators performed worse.
Spatial optimization is promising and allows simulation of forest management at the enterprise level (case study) with ideal management allocation.
Project description
Sustainable forest management includes, in addition to timber production, the promotion of biodiversity as well as the provision of multiple important ecosystem services such as recreation, protection against gravitational natural hazards and carbon storage. The central task of forest planning and management is to ensure the sustainable provision of these ecosystem services. For many of these ecosystem services, however, an adequate valorization is still missing on the operational level, which is why their participation in the operational success can only be considered indirectly. In order to include these aspects in forest planning, suitable concepts and instruments for decision support are needed. For this purpose, such a decision support system (DSS) was developed in a previous project at WSL (Blattert 2020).
This DSS convinces by a differentiated evaluation and representation of the influence of different treatment strategies on the sustainability of management under economic, ecological and social aspects. However, the need to increase the flexibility to apply the model to additional study areas, as well as the extension of the model to possible strategy mixtures, was particularly highlighted by Blattert as a need for future research.
The goal of this project was to adapt the model and add a spatial optimization algorithm so that it automatically determines the ideal mix of different management strategies. Furthermore, adjustments in the form of the calculations increased the flexibility regarding the inclusion of further indicators such as capercaillie and the application to further case studies. At the same time, a new management strategy was implemented with plenter management, namely with permanent forest management, which is of great relevance for many areas in Switzerland. The model now represents both even-aged and uneven-aged management models, respectively age class and plenter forests.
In a first step, the data from the sample inventories of the study areas were prepared and virtual stands were generated from them in the forest growth simulator "Waldplaner" and the future forest development under the given silvicultural treatment strategies was simulated over 50 years. In a second step, the model was modified to calculate the indicator and utility values (so-called utility values) at the sample level and to generate the input data for the spatial optimization.
In a third step, a spatial optimization model was developed. This minimizes the deviations of the different indicators from a best achievable value. The optimization model assigns a management strategy to each sample.
Finally, the resulting overall system was applied and graphically evaluated for the two study areas Bülach (ZH) and Gottschalkenberg (ZG). The aim was to determine the ideal mix of treatment strategies as well as their spatial distribution that best meets the individual demands of the forest managers.
Conclusions
Among the management strategies considered, it was striking how well the scenarios without clearing (no management, plentering/permanent forest) performed in comparison to the classic management strategies in high forest. Furthermore, a trade-off between timber production and the indicator groups recreation, biodiversity and carbon storage resulted, i.e. in a strategy where timber production performed well, the other indicators performed worse. This behavior is partly due to the comparatively short simulation periods (in 5-year increments, up to 50 years). In the spatial optimization, each sample plot was assigned a management strategy so that benefits were maximized over the entire case study. The sample plots used from the inventories represent 1.2 ha (grid 80m x 150m) for the Bülach forestry operation and 1 ha (grid 100m x 100m) for Gottschalkenberg (Canton Zug). The stands simulated with the forest planner in the study are 0.2 ha in size. This classification proved to be too detailed for implementation. An aggregation for larger stands or management units would be more target-oriented.
Spatial optimization is promising and allows for simulation of forest management at the enterprise level (case study) with ideal management allocation. This process makes it possible for forest planners and decision makers at the inter-company and company levels to also assess mixed silvicultural strategies (e.g., high forest and plenter forest/permanent forest) and their impacts on the provision of biodiversity and ecosystem services (BES). In particular, the possibility to use other indicators besides the classical cutting rate to assess sustainability was highly appreciated by the practice partners.
Due to the high complexity of the question, it was not yet possible within the scope of this project to provide an operationally applicable optimization tool for practice. However, based on the feedback from the field, the relevant factors (need for action) were identified and narrowed down and will thus be included in the development of further projects.
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You can also find the complete project report on ARAMIS.
The project was supported by the Forest and Wood Research Funding Switzerland FOWO-CH of the Federal Office for the Environment FOEN and the Conference for Forests, Wildlife and Landscape KWL of the cantons.