In charge of the project
Prof. Dr. Paul Magdon
Funding
MWK - Ministry of Science and Culture of Lower Saxony
Project Management Agency
Universität Göttingen
Project costs
530.368,43 €
Duration
13.08.2024 until 31.10.2030

FoResLab – Future Lab towards Forests Resilient to Climate Change

Climate change directly affects the forests in Central Europe and challenges the way how they are managed under current and future conditions
To address this challenge, FoResLab – Future Lab towards Forests Resilient to Climate Change – has been established as a dedicated platform in Lower Saxony. Through a highly inter- and transdisciplinary approach, FoResLab tackles the fundamental question: How can we make forests resilient to climate change under current and future conditions?
By bringing together experts from universities, research institutions, international collaborators, and practice partners, FoResLab ensures a strong connection between science, the private sector, and civil society. Organized into three platforms and 13 subprojects, it enhances innovative methods in research, science communication, and knowledge transfer.

HAWK’s Contribution: Advancing Remote Sensing for Forest Resilience

As part of FoResLab, HAWK is leading the subproject “Development of a Remote Sensing Indicator Framework to Monitor Forest Resilience”. By integrating forest inventory data with advanced remote sensing technologies such as airborne laserscanning, this project aims to establish a comprehensive framework for efficiently assessing key variables of forest resilience at the landscape scale.
The results will provide detailed, state-level maps of tree species composition, forest structure, and vitality—offering crucial insights to support sustainable forest management in a changing climate.

3D point cloud from the Solling Nature Park

Using airborne laser scanning, comprehensive maps of the structural properties for the entire state of Lower Saxony are being created.

Transect of a terrestrial laser scanning recording

The data from terrestrial laser scanning is used to train a machine learning model capable of predicting structural forest characteristics.

Aerial image from the Hainich National Park

In addition to the remote sensing studies conducted by HAWK, further data is collected at the Eddy Flux towers, such as the fluxes of carbon dioxide and water vapor.