Digital forest inventory in the Rosalia teaching and training forest

Forest inventories are a key component of sustainable forest management, yet traditional approaches based on manual measurements are time-consuming, labor-intensive and limited in spatial resolution. Recent advances in laser scanning technologies, including terrestrial laser scanning (TLS), personal laser scanning (PLS), and drone-based LiDAR, provide new opportunities for high-resolution, efficient and reproducible forest data acquisition. The aim of this project is to further establish a digital forest inventory for the research and teaching forest Rosalia of the BOKU University (BOKU). Using a combination of TLS, PLS, and drone-based LiDAR data, detailed individual tree attributes such as tree position, diameter at breast height (DBH), tree height, stem volume and crown characteristics will be derived. These data will be integrated with existing datasets collected over recent years to build a comprehensive and consistent database. Based on these data, spatially explicit models of stand-level parameters such as growing stock, basal area, and mean tree dimensions will be developed. The resulting digital dataset will serve as a foundation for research, teaching, and forest management decisions in the research and teaching forest Rosalia. This project contributes to the ongoing digital transformation of forest inventory by providing a practical and transferable workflow for integrating multi-source LiDAR data into forest monitoring and decision support systems