Decision Support Systems for Sustainable Forest Management
Summary
Decision support systems (DSS) for sustainable forest management integrate data analytics, simulation modelling and optimisation algorithms to guide strategic and operational choices in forest ecosystems. By combining forest inventory databases, remote sensing outputs and growth‐and‐yield models, these systems enable managers to evaluate trade-offs among timber production, carbon sequestration, biodiversity conservation and recreation. Modern DSS platforms often embed multi-criteria decision analysis to reflect stakeholder values and policy objectives, and stochastic programming or robust optimisation methods to account for uncertainties in growth rates, market prices and climate change scenarios. Geographical information systems (GIS) provide a spatially explicit interface, while web-based group-decision modules support participatory planning. Applications range from national policy assessments to stand-level harvest scheduling, facilitating adaptive management and evidence-based risk mitigation. By linking ecological models with economic and social criteria, DSS strengthen the capacity of forest managers to balance competing objectives, anticipate long-term outcomes and contribute to global targets for climate change mitigation and biodiversity preservation.
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Decision Support Systems for Sustainable Forest Management publication trend
The graph below shows the total number of articles in decision support systems for sustainable forest management across all publications each year (not limited to Nature Index journals).
Technical terms
Decision Support System (DSS): An integrated software environment combining data inputs, models and analytical tools to assist decision makers.
Multi-criteria Decision Analysis (MCDA): A structured approach to evaluate and rank alternatives based on multiple, often conflicting objectives.
Stochastic Programming: An optimisation framework that incorporates uncertainty by considering multiple scenarios of future states.
Robust Optimisation: An approach to find solutions that remain effective under a range of uncertain parameter variations.
Pareto-optimal: A state where no objective can be improved without worsening another, defining a frontier of best trade-off solutions.
Value of Information (VoI): A metric quantifying how much decision outcomes improve when additional data are acquired.
Geographical Information System (GIS): A technology for capturing, storing and visualising spatially referenced data.
Simulation Modelling: The use of computational models to project system behaviour over time under various management regimes.
References
- The multi-faceted Swedish Heureka forest decision support system: context, functionality, design, and 10 years experiences of its use. Frontiers in Forests and Global Change (2023).
- Multi-objective optimization of forest ecosystem services under uncertainty. Ecological Modelling (2024).
- Planning cost-effective operational forest inventories. Biometrics (2024).
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