GreenForCE

Wood biomass is the most important bioenergy source in Austria and the EU and therefore plays a crucial role in the energy transition. However, balancing the need for bioenergy production with the conservation of forests as vital carbon storage ecosystems presents a key challenge. To ensure the carbon storage function, EU legislation addressing LULUCF, climate policy and the bioenergy sector, as well as the EU Forest Strategy for 2030, foresee the use of damaged forest biomass (e.g. due to windthrow or insect infestation) and wood residues as primary biomass sources for energy production, while avoiding the use of wood biomass from intact and healthy forests. Addressing this challenge requires accurate and continuous monitoring of forest status and forest biomass resources to ensure sustainable and climate-neutral bioenergy management.

With project GreenForCE, we address these new requirements by combining Austrian airborne LiDAR data (ALS) with the high temporal resolution of satellite earth observation data. This integration ensures the measurement of above-ground biomass and the timely identification and classification of forest disturbances. This remote sensing solution was originally drafted as part of the Space4Energy Hackathon, co-organized by ESA in 2022. An optimised multi-source approach now integrates satellite data, ALS data, forest inventory data and forest growth models to enable more precise biomass estimates with a significantly improved accuracy compared to existing satellite-based products. Time-series analysis of Sentinel-2 imagery enables the detection of forest damage, while forest characteristics such as tree species, stand age and height are combined with biomass data to accurately quantify wood volume and potential wood residues. Empirical data on tree species-specific harvesting and processing residues (e.g. in sawmills and during pulping) are used to model and map potential primary and secondary wood processing residues that could be used locally for bioenergy production. A web-based framework combines these results with infrastructure data, such as the locations of sawmills and bioenergy plants. This supports decision-making processes that enable sustainable resource management and strategic planning for the green energy transition.