Research

Forests bind 25% of annual carbon emissions and an increase in forest area and an increase in forest growth contribute to the EU Forest Strategy 2030 and national strategies (climate neutrality by 2040, carbon management strategy). One of the greatest uncertainties in such plans is the response of the forest to global warming, the mechanisms of which are only partially understood so far. Trees' responses to climate can best be determined using high-resolution dendrometer and sap flow measurements. However, such detailed measurements are only possible at a few locations and on a few trees. In contrast, satellite image data have good spatial representativeness and vegetation dynamics can be well mapped using time series from satellite image data. However, no causal relationships to forest growth can yet be established from these time series. The aim of the project is to develop a spatially and temporally high-resolution monitoring of the growth and tree species vulnerability for the tree species spruce, fir, larch, pine, beech and oak in pure and mixed stands by combining dendrometer data, sap flow sensors and satellite images and to test it on independent data.

The assessment of site productivity (also referred to as site quality, site potential, site index, or yield class) is a fundamental pre‐ requisite for sustainable forest management. In climate-resilient forests, characterized by increasingly complex structures — dri‐ ven by mixed species stands and the necessity to respond to or mitigate disturbances such as drought, pests, or storm damage — traditional methods of forest inventory and site assessment are reaching their limits, being feasible only at high cost and with substantial effort. The aim of this project is therefore to develop innovative approaches that allow site productivity to be recorded digitally in an efficient, flexible, and large-scale manner. The project focuses on three methodological pillars: Remote sensing as an efficient digital tool for growth monitoring Laser scanning will be used to accurately determine growth parameters such as whorl spacing and height increments, both through terrestrial and personal laser scanning (TLS/PLS) as well as by comparing temporally repeated airborne laser scanning surveys (ALS). Complementary, digitally recorded dendrometric parameters at the tree and stand level will be integrated in order to derive site productivity independently of stand age through forest growth models and to represent it spatially at high resolution using modern statistical methods. eDNA as a site indicator The project will evaluate the potential of environmental DNA (eDNA) from soil samples as an integrative measure of traditional site characteristics (e.g., nutrient or water availability). The reliability of this approach will be tested and validated through com‐ parison with established site data and maps. Integration of site and yield data The systematic integration of eDNA, classical site parameters, and laser-derived growth data will serve as the basis for large-scale modeling of forest productivity. This will be achieved by combining terrestrial measurements with spatially extensive airborne laser scanning data, supported by modern statistical techniques

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