Research

This research project is carried out in collaboration with the HBLFA Raumberg-Gumpenstein. The research focuses on evaluating sustainable nutrient management on alpine pastures through site-specific management strategies. These results will inform the HBLFA Raumberg-Gumpenstein's contributions to the Alpine Pasture Evaluation Study (APES), which is funded by the Federal Ministry for Agriculture, Regions and Water Management (BML). BOKU is involved in two work packages related to remote sensing and geospatial analysis for yield estimation and change detection in pasture spatial structures.

The research contract with 'Die Bundesanstalt Statistik Österreich' focuses on developing algorithms and data access structures to apply existing models for calculating annual yields and feed quality (crude protein content) in various grassland cutting systems. The know-how was developed within the FFG ASAP SatGrass project at the Institute of Geomatics. The research services include the extension of the following aspects: - Database Evaluation: Assessing a database for SatGrass model parameters using satellite data at the parcel level (IACS-GIS). - Start of Growing Season (SOS) Estimation: Developing algorithms to determine SOS for mown meadows using alternative satellite data, replacing the limited-time availability of MODIS data, and generating SOS time series. - Cut Detection Enhancement: Advancing cut detection methods based on Sentinel-1 and Sentinel-2 time series.

Society faces the urgent challenge of developing sustainable, equitable, and scalable solutions to climate change, driven by the critical need for evidence-based management of geo-ecological life support systems like carbon, water, and heat. To address this need, we will develop scale-aware benchmarks for land carbon sequestration by leveraging atmospheric CO2 flux ground truth data alongside a wide range of in-situ and remotely sensed earth observations. We will provide an independent, impartial and actionable benchmark with uncertainty estimates to link activity-based bottom-up inventories and top-down atmospheric concentration inversions. This benchmark is essential for accurate global-to-ownership-level carbon accounting, and unlocking the development and improvement of effective nature-based climate solutions. Our project will initiate a paradigm shift from the current practice of scale-agnostic data joins to a scale-aware methodology from local to global levels. This new machine learning benchmark will maximize the use of scarce flux ground truth observations, consistently integrate multi-source earth observations, increase statistical power, and reconcile flux estimates across scales.