Remote sensing and machine learning-based approaches are key to detect, predict and analyse changes in forests under climate change. The training offered in this summer school will include theory lectures, on climate resilience and remote sensing, practical exercises and a field trip to disturbed areas. Other subject studies are dynamics of boreal forests and forestry in the boreal region.
Participants will have the opportunity to work in groups, learning how to retrieve remote sensing data, detect and analyse forest change, classify data, as well as making predictions of forest damage (i.e. disturbances).
The summer school will be organised at the University of Eastern Finland in Joensuu Finland, in collaboration with INRAe and the Horizon Europe Eco2adapt project and the support from IUFRO Division 8 on Forest Environment.
The programme will take place from 7 to 18 August 2023.
The main topical contents will be:
- Forestry in boreal forests, Finland
- Which forests are prone to disturbances in boreal areas?
- Planetary Computer and remote sensing data
- Monitoring disturbances and their management (i.e. mitigation measures)
- Machine learning theory
- Change detection analyses
The person in charge for this summer school is Frank Berninger () and the involved experts include Frank Berninger (UEF, Finland), Blas Mola (UEF, Finland), Dino Ienco (Inrae, TETIS, France), Kenji Ose (Inrae, TETIS, France).