Research topics
Table of contents
Research topics
The core research areas of the Chair of Environmental Remote Sensing are the development of methods and application of satellite data and derived products for the observation, analysis, modeling and prediction of processes and changes in ecosystems and their influence on the global carbon and water cycle and the climate. One core research area is the observation and prediction of vegetation fires. We use and develop data-based methods of time series analysis, machine learning, artificial intelligence, retrieval of environmental parameters with physical and data-driven model, as well as process-based environmental models and model-data integration techniques.
Forschungsthemen der Professur mit räumlichen Skalen und Methoden sowie assoziierte Projekte
Microwave remote sensing for forest-water interactions
The goal is to develop an understanding of the sensitivity and physical relationships between observations from active and passive microwave satellites and leaf moisture content, vegetation water content, interception and evapotranspiration, and vegetation phenology. This research makes it possible to quantify the amount of water and biomass in terrestrial vegetation based on microwave satellite observations and to understand their interactions with the local to global carbon and water cycles and to quantify them for the future.
The topic is further developed in close coordination with the potential area "Forest-Water-Dynamics" of the Excellence Strategy of TU Dresden.
Participating staff
- Evripidis Avouris: Sensitivity of radar UAVs to forest-water dynamics
- Matthias Forkel: Development of modelling concepts to retrieve live fuel moisture content and vegetation water content from passive microwave vegetation optical depth
- Johanna Kranz: Retrieval of phenology, live fuel moisture content and interception from Sentinel-1 time series
- Xiao Liu: Estimation of vertical structure and biomass distribution in forests from radar and lidar data
- David Moravec: Estimation of vegetation water content with Sentinel-1 and corner reflectors
- Luisa Schmidt: Investigation of global dynamics in live fuel moisture content with long-term time series of vegetation optical depth
Data sets and results
- VOD2LFMC: Global leaf moisture content zenodo.org
- Current measurement data from our fire weather stations in the Saxon Switzerland/Eastern Ore Mountains district: emsbrno.cz
Selected publications
Forkel, M., Schmidt, L., Zotta, R.-M., Dorigo, W., and Yebra, M. (2023).
Estimating leaf moisture content at global scale from passive microwave satellite observations of vegetation optical depth
Hydrology and Earth System Sciences, 27, 39-68, https://doi.org/10.5194/hess-27-39-2023
Liu, X., Neigh, C. S. R., Pardini, M., and Forkel, M. (2024).
Estimating forest height and above-ground biomass in tropical forests using P-band TomoSAR and GEDI observations
International Journal of Remote Sensing, 9, 45, 3129-3148, https://doi.org/10.1080/01431161.2024.2343134
Schmidt, L., Forkel, M., Zotta, R.-M., Scherrer, S., Dorigo, W. A., Kuhn-Régnier, A., van der Schalie, R., and Yebra, M. (2023).
Assessing the sensitivity of multi-frequency passive microwave vegetation optical depth to vegetation properties
Biogeosciences 20, 1027-1046, https://doi.org/10.5194/bg-20-1027-2023
Remote sensing for forest fire research and management
The aim is to combine different observations from optical and microwave remote sensing and to develop integrative methods to quantify and monitor different aspects of forest fires such as fuel load, fuel moisture, fire danger, fire behaviour and emissions of smoke and greenhouse gases.
As part of this line of research, we work closely with international research groups on the global carbon cycle and the European Space Agency on the one hand, and with practitioners from fire and forestry authorities and public administration on the other.
Among other things, the Chair has developed the globally unique and leading TUD-S4F approach, which enables the global quantification of fuel loads, fire behavior and forest fire emissions at high spatial resolution (Forkel et al. 2025).
Participating staff
- Matthias Forkel: Large scale fuel, fire and fire emission monitoring and modelling; role of fire in the carbon cycle; model-data integration for dynamic global vegetation-fire models
- Daniel Kinalczyk: Assessing the effect of shrub encroachment on fuel loads and fire emissions; development of the S4F data-model fusion approach
- Johanna Kranz: Dynamics of fuel moisture and fire weather in Central Europe
- Xiao Liu: Mapping shrubs and understorey fuels for fire emission estimation
- Christopher Marrs: Cross-boundary assessment, communication and management of wildfire risks in Central Europe
- Luisa Schmidt: Global dynamics in live fuel moisture content; modelling peat soils to estimate fire emissions
Data sets and results
- Sense4Fire: Experimental database for fuels and fire emissions sense4fire.eu/database
- Fires in Bohemian-Saxon Switzerland 2022: fuel types, fire severity, fire radiation power zenodo.org
Selected publications
Beetz, K., Marrs, C., Busse, A., Poděbradská, M., Kinalczyk, D., Kranz, J., and Forkel, M. (2024).
Effects of bark beetle disturbance and fuel types on fire radiative power and burn severity in the Bohemian-Saxon Switzerland
Forestry: An International Journal of Forest Research, cpae024, https://doi.org/10.1093/forestry/cpae024
Forkel, M., Wessollek, C., Huijnen, V., Andela, N., de Laat, A., Kinalczyk, D., Marrs, C., van Wees, D., Bastos, A., Ciais, P., Fawcett, D., Kaiser, J.W., Klauberg, C., Kutchartt, E., Leite, R., Li, W., Silva, C., Sitch, S., Goncalves De Souza, J., Zaehle, S., Plummer, S. (2025).
Burning of woody debris dominates fire emissions in the Amazon and Cerrado.
Nature Geoscience, 18, 140-147. https://doi.org/10.1038/s41561-024-01637-5
Kranz, J., Bauer, K., Pampanoni, V., Zhao, L., Marrs, C., Mauder, M., Poděbradská, M., van der Maaten-Theunissen, M., Yebra, M., and Forkel, M. (2025).
Assessing predictors for fuel moisture content in Central European forests.
Agricultural and Forest Meteorology, 371, 110590, https://doi.org/10.1016/j.agrformet.2025.110590
Remote sensing for landscape change and agriculture
The aim is to use remote sensing data to detect historical to short-term changes in (agricultural) landscapes and to support decisions in agriculture, nature prottection and landscape planning. In particular, methods of image analysis and artificial intelligence are used. For example, historical spy images from the 1960s were analyzed using deep learning methods to map changes in landscapes throughout Saxony. Current projects with the Saxon State Office for Environment, Agriculture and Geology focus on developing methods for monitoring the good agricultural and ecological condition of agricultural landscapes.
Participating staff
- Eric Kosczor: Mapping long-term structural changes in agricultural landscapes; detection of anomalies in eco-agricultural management
- Christopher Marrs: Transfer of remote sensing approaches for biodiversity monitoring, change detection and lndscape planning to stakeholders
- Christine Wessollek: Applying remote sensing in agricultural monitoring; integration of remote sensing with ecohydrological models for soil moisture monitoring in agriculture and forestry
Data sets and results
- Historical "key hole" images from the 1960s and 1970s in the Saxony Atlas geoportal
- Phenology of the land surface of Araucaria nothofagus forests in the Andes zenodo.org
Selected publications
Kugler, L., Marrs, C., Kosczor, E., and Forkel, M. (2023).
Land cover in Saxony 1961-1979 (Part 1). Detection of land cover changes in Saxony 1961-1979: Analysis using historical CORONA spy images and deep neural networks
Publication series of the LfULG, 6/2023, 72, https://publikationen.sachsen.de/bdb/artikel/42436
Kosczor, E., Forkel, M., Hernández, J., Kinalczyk, D., Pirotti, F., and Kutchartt, E. (2022).
Assessing land surface phenology in Araucaria-Nothofagus forests in Chile with Landsat 8/Sentinel-2 time series.
International Journal of Applied Earth Observation and Geoinformation, 112, 102862, https://doi.org/10.1016/j.jag.2022.102862