Engineers and ecologists are developing the EDE 4.0 forest management assistance system. Powered by artificial intelligence (AI), it helps conserve and manage forests sustainably.

Climate change is affecting forests around the world. Germany is now experiencing the largest deforestation since the 1980s. In Baden-Württemberg, 43% of all forest areas are damaged. It should be borne in mind that many factors have to be taken into account when managing forests.

“Mixed forests with hornbeam, maple or bird cherry are much better suited to new conditions than spruce forests, but less profitable. In addition, soil properties play an important role in tree planting, explains Dr. Joachim Fahlmann from the South German Climate Office KIT. “Forest management must be responsive and carefully balance the various aspects.”

EDI GmbH, the South German Climate Office and the KIT Institute of Geography and Geoecology are developing a cloud-based AI-based decision support system. The goal is to help foresters make the best decisions based on data. The interdisciplinary EDE 4.0 project works closely with partners in the forestry sector to support sustainable forestry.

The help system is based on a software solution from EDI GmbH. The EDI hive IoT framework was originally designed for machine learning in aerospace and mechanical engineering. “The result of our development work will be a mobile application that can be operated intuitively. AI works to support local foresters in deciding where and when to plant new trees. Moreover, the system will predict how successful planting a tree will be in a specific location, ”explains Dr. Thomas Freudenmann, one of the founders of EDI GmbH.

For the system to deliver relevant results, it must first recognize relationships and patterns. For this, many data from different areas are combined. Among them are data on the medium-term climate development provided by the German Meteorological Service and the Institute for Meteorology and Climate Research KIT. In addition, the assistance system will take into account the local knowledge and experience of foresters.