Research

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

Green roofs with large trees are becoming increasingly important in the context of urban climate adaptation. However, there is still a lack of sound scientific data on the actual growth, biomass development, and CO₂ storage capacity of trees in artificial locations such as roofs and underground parking garages. Load assumptions currently in use are based on outdated or incomprehensible reference values, which often leads to significant overestimations of tree mass. This results in oversized support structures, increased material use, and unnecessary costs and CO₂ emissions. The aim of this research project is to collect reliable data on the morphology and mass distribution of large trees on buildings for the first time. To this end, around 30 trees on four completed projects in Austria will be measured using modern laser scanning technology. The surveys will be carried out twice a year – in leafy and leafless conditions – in order to record seasonal differences. The trunk, branch, and foliage biomass as well as the potential CO₂ sequestration will be analyzed. The results should reveal differences in morphology compared to trees growing on natural soil and provide a basis for future modeling of tree growth on roofs. In the long term, the results obtained should be incorporated into regulations and increase planning reliability. This should contribute to the wider use of intensive green roofs and make better use of their potential for urban cooling, rainwater retention, and CO₂ reduction.

The measurement of timber is of central economic importance in forestry and the wood industry. In practice measuring harvested wood is time-consuming and thus expensive. Forestry companies therefore usually rely on the wood industry's measurement lists to determine the exact quantities of wood harvested. In the course of this project, algorithms for the automatic measurement of wood stacks recorded with laser scanners, are going to be developed. The aim is to enable the transparency of timber flows along the supply chain and advance the digitization of it. This will be achieved in three sub-goals: 1. Individual log measurement of higher-value round timber assortments An overview of the quantities of timber harvested and delivered is of particular economic interest in the case of high-value and sawn round wood. For this type of stacked wood, it should be possible to generate measurement results on an individual log basis and thus obtain an overview of diameter and volume distributions, assortment lengths and the total stack volume. 2 Objective determination of conversion factors for energy and industrial wood Various conversion factors are used when converting the gross volume into the net volume. The choice of the factor is usually based on subjective judgement though. By collecting parameters from laser scans, it should be possible to determine an individual conversion factor for scanned wood stacks and thus enable an objective, comprehensible volume determination for energy and industrial wood stacks. 3. feature recognition of stacked logs The composition of tree species and the proportion of lower-value assortments are important factors in the invoicing of wood. However, an exact survey of the volume shares of these assortments is time-consuming outside of sawmills. These variables are therefore usually estimated. Using algorithms developed in this project, the estimation of tree species and value-reducing properties such as wood decay or blue stain will be automated using laser scanning data. This should be possible for energy and industrial wood as well as saw logs. Together with the evaluation of individual log sizes, it should ultimately be possible to create a list of dimensions for stacks of saw logs, as is known from sawmills.