MW3 Standardisiertes Monitoring von Wachstumsreaktionen wichtiger Waldbaumarten auf klimatische Extremereignisse [funded by BMEL]

Project staff:

Dr. Boris Thies
Marvin Muesgen-von den Driesch


Research co-operation

Prof. Dr. Michael Leuchner, LFG Physische Geographie und Klimatologie, RWTH Aachen University
PD Dr. Heye Bogena, Prof. Dr. Harrie-Jan Hendricks-Franssen, Institut für Bio- und Geowissenschaften Agrosphäre IBG-3, Forschungszentrum Jülich GmbH
Dr. Theresa Blume, Hydrologie, PD Dr. Ingo Heinrich, Klimadynamik und Landschaftsentwicklung, Helmholtz-Zentrum Potsdam - Deutsches GeoForschungsZentrum GFZ
Prof. Dr. Andreas Huth, Dr. Friedrich Bohn, Department Ökologische Systemanalyse, Dr. Corinna Rebmann, Hydrosystemmodellierung, Helmholtz-Zentrum für Umweltforschung GmbH – UFZ

German forests have been exposed to several years of droughts since 2018. The droughts caused forests to lose around 500,000 hectares of their canopy area in 2018-2021 alone. Enormous adjustments are being made to the future of forestry in terms of forest management. One of he significant challenges for the future will be the development of operational forest monitoring systems that are sensitive to the effects of extreme climate events.


Aim of the joint project:

The main objetive of the project is to develop a standardised monitoring system for recording and analysing growth responses of important Central European tree species with a focus on extrem climatic events. The system is designed to capture the most important key parameters for a cross-scale growth analysis by closely coupling ground measurement, satellite based remote sensing and tree modelling. For this purpose, the different growth responses of forest tree species in the german low mountain range are analysed.

Aim of the LCRS in sub-project 3 - Upscaling and forecasts:

The main goal of the LCRS in the project is to model growth response data, such as sap flow and tree thickness growth, in high spatial and temporal resolution. For this purpose, data from the latest generation of remote sensing systems will be combined with in-situ data from the new DHC station developed as part of the overall project. Using machine learning methods and image fusion techniques, the temporal and spatial resolution of the resulting data will be increased, enabling daily monitoring at plot and individual tree level.

The research is funded by the Forest Climate Fund (project MW3; grant agreement 2220WK86C4).

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