Forest play important role in climate regulation and secure resources vital for local economy,  but the imminent climate change will affect their productivity, and for some tree species possibly even their sustainability. Thus, monitoring forest productivity and understanding effects of key environmental drivers becomes essential for forest management. This is a challenging task, particularly due to great costs related to it.
This research, aimed at addressing this issue, provides a novel approach that could facilitate fast and cost-effective assessments of forest productivity. The research would be conducted on pedunculate oak forests (Quercus robur L.) which are economically, and ecologically, one of the most important in Croatia.
Main objective of the research is to develop and test methods and workflows for the estimation of annual productivity of forests at the local scale by using existing research infrastructure for measurement of carbon fluxes (EC monitoring system), freely available high temporal resolution data from remote sensing (MODIS) and state of the art process model (Biome-BGC). The research will provide the first evaluation on the quality of MODIS GPP and NPP estimates for Central-European lowland forests. Field measurement campaigns combined with state of the art digital photogrammetry will be performed to parameterise Biome-BGC model, and validate data from MODIS and model simulations. Using available data on future climate scenarios, productivity of oak forests for the future 30-50 years will be simulated with Biome-BGC model.