Laboratory for
Climatology and Remote Sensing

Project staff:

Marvin Muesgen-von den Driesch
Céline Müller

Abstract:

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

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

Description:

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. In addition, tree growth will be quantified using dendrochronological data (tree-ring measurements) as well as continuous dendrometer measurements, which allow high-frequency monitoring of stem diameter variations. To generate continuous time series, active and passive sensor systems will be fused to produce cloud-free, temporally highly resolved image products. Using machine learning and deep learning methods, these heterogeneous data sources are integrated and modeled to upscale growth-related parameters such as stem diameter increment and sap flow from the tree level to the forest stand and landscape scale, enabling spatially continuous monitoring of forest growth dynamics.

Publications:

- Konstantin Engelmayer, Marvin Müsgen-von den Driesch, Burkhard Neuwirth, Kai von den Driesch, Jörg Bendix, and Boris Thies. 8-day multisensor derived biophysical index groups and their ability to explain tree ring growth. SSRN Preprint, 2026. doi:10.2139/ssrn.6331732 . Preprint, under review at Science of Remote Sensing.
- Müsgen-von den Driesch, M., Bendix, J., and Thies, B.: Two New Radar Vegetation Indices (RVVI and RSVI) for Reconstructing NIRv as an Indicator of Tree Growth, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-9636, https://doi.org/10.5194/egusphere-egu26-9636 , 2026. Two New Radar Vegetation Indices (RVVI and RSVI) Poster

Bachelor- and Masterthesis projects :

Finished:

-Beyond Greenness: Biophysical Index Groups and Their Ability to Explain Tree-Ring Growth Using a 23-Year Earth Observation Data Cube - Konstantin Engelmeyer
-Time series based tree species classification of Central European tree species in German low mountain regions using dynamic time warping - Lars Pehoviak

In progress:

- Drought responses of larch across an east-west climatic gradient in Central Europe: Combining short-term PlanetScope vegetation index dynamics and long-term radial growth - Christine Meng

Open Thesis Topics:

- Estimation of Tree Water Deficit from Dendrometer Measurements Using Satellite Remote Sensing Data under Drought Stress Conditions
- Evaluating the Linear and Nonlinear Relationship between ECOSTRESS-Derived Evapotranspiration and Sap Flow Measurements under Varying Climatic Conditions
- Development of a Workflow for Generating a Weekly National Near-Infrared Reflectance of Vegetation (NIRv) Dataset from 2014 to Present Using Google Earth Engine
- Linking SAR-Derived Radar Vegetation Indices (RVVI, RSVI) with Radial Tree Growth from Dendrometers and Tree Ring Index
- Linking Sentinel-1 SAR Backscatter to Tree Water Dynamics: A Multiscale Analysis of Sap Flow and Stem Water Deficit Derived from Dendrometer Measurements