Research Topics

Effective forest management requires understanding how forest stands grow and coordinating management decisions across spatial and temporal scales. We develop growth and yield models, stand density management tools, and dynamic models to evaluate stand development, thinning regimes, and harvesting strategies. At broader scales, we apply simulation, linear programming, and other optimization methods to support forest- and landscape-level planning under multiple objectives and constraints.

Precision forestry provides the detailed and timely information needed for effective forest management. We use remote sensing, LiDAR, UAVs, field measurements, and data analytics to characterize forest structure, monitor change, and evaluate management outcomes. These technologies support data-informed and site-specific decisions in forest inventory, silviculture, harvesting, and planning.

Understanding the forest value chain is vital for optimizing resource use, promoting sustainable practices, and identifying opportunities for innovation and efficiency improvements in the forest industry. It also helps in creating a balance between economic growth, social well-being, and environmental conservation within the forestry sector.

Regional circular forestry is an innovative approach to forest management that focuses on creating a circular economy within a specific geographic area. By embracing circular principles, it aims to create a regenerative and sustainable forest-based economy that benefits both nature and local communities in the long term.