ALT Thanks @stefanopuliti, @carolinfischer and Mostafa Hoseini for the images

ALT Fatemeh takes part in machine learning and image recognition tasks within SmartForest. One of her work areas is the development of learning-based systems for the automated recognition of branches in different seasons. Applications can be for example damage registrations after a windfall or snow break.

ALT The main goal of the PhD is to improve operational forest management information using remotely sensed data to support precision forestry with improved accuracy and precision while reducing inventory costs. Improving the accuracy and precision of the forest attribute estimates and quantifying their uncertainties are essential to improve management decisions. Specific objectives include evaluating different methods and data sources (aerial imagery, optical satellites, airborne laser scanning) to provide tree species information at an operational level; characterizing the uncertainty of stand-level forest site index estimates in Forest Management Inventories, (FMI); and developing a system for location of field sample plots in FMIs in real-time using auxiliary data.

ALT Maria works on improving the accuracy and precision of site index estimation with remotely sensed data. So far, she has focused on classifying areas unsuitable for site index determination due to disturbances and how we might determine the site index when we have time series of remote sensing data.