Section 1.4: Remote Sensing and Geoinformatics

The research aims of the Remote Sensing and Geoinformatics Section at GFZ are to establish remote sensing as a core method in geosciences. In particular, we aim to increase awareness of the considerable value of remotely sensed data for knowledge generation about Earth’s surface properties and processes, which arises from its ability to provide complete coverage over large spatial scales. Our research and method development covers the entire range of the remote sensing processing chain. We examine bio, geophysical, and geochemical processes in soil, geology, vegetation, and the atmosphere which are triggered by landscape and vegetation development, climate change, natural disasters, and human land use.

Our work on monitoring bio and geophysical surface parameters includes developing sensors for mapping change from satellite, aircraft, and drone imagery. We also develop methods for simulation, calibration, and fusion of data from multiple (optical and radar) sensors via spectral modeling. We investigate the connection between bio and geophysical processes and spectral imagery by combining spectral measurements in the laboratory, in the field, and from air and spaceborne systems with the physical and chemical properties of real surfaces, which we sample using field surveys. Our image analysis methods and software extract information about changes from time series data, enabling users to identify the underlying bio and geophysical processes. This work in particular involves the use and adaptation of big data analytics and data science approaches.

What makes our section exceptional is our multifaceted expertise in remote sensing. This allows us to first observe changes in imagery, and then to understand the underlying process responsible and to observe this process continuously through long-term monitoring.

An unprecedented amount of imagery and remotely sensed data is available today, and this information could benefit many people who are not experts in remote sensing. We therefore founded the “FERN.Lab”, to ensure that our scientific work is translated into practical applications that benefit society.

We hold the role of science principal investigator for the German hyperspectral satellite mission EnMAP. Our diverse expertise covering the entire remote sensing chain (from sensor to application) is pooled together in the development of this mission.


Causality guided machine learning model on wetland CH4 emissions across global wetlands

Yuan, K., Zhu, Q., Li, F., Riley, W. J., Torn, M., Chu, H., McNicol, G., Chen, M., Knox, S., Delwiche, K., Wu, H., Baldocchi, D., Ma, H., Desai, A. R., Chen, J., Sachs, T., Ueyama, M., Sonnentag, O., Helbig, M., Tuittila, E.-S., Jurasinski, G., Koebsch, F., Campbell, D., Schmid, H. P., Lohila, A., Goeckede, M., Nilsson, M. B., Friborg, T., Jansen, J., Zona, D., Euskirchen, E., Ward, E. J., Bohrer, G., Jin, Z., Liu, L., Iwata, H., Goodrich, J., Jackson, R. (2022): Causality guided machine learning model on wetland CH4 emissions across global wetlands. - Agricultural and Forest Meteorology, 324, 109115.


Abstract: Wetland CH4 emissions are among the most uncertain components of the global CH4 budget. The complex nature of wetland CH4 processes makes it challenging to identify causal relationships for improving our understanding and predictability of CH4 emissions. In this study, we used the flux measurements of CH4 from eddy covariance towers (30 sites from 4 wetlands types: bog, fen, marsh, and wet tundra) to construct a causality-constrained machine learning (ML) framework to explain the regulative factors and to capture CH4 emissions at sub-seasonal scale. We found that soil temperature is the dominant factor for CH4 emissions in all studied wetland types. Ecosystem respiration (CO2) and gross primary productivity exert controls at bog, fen, and marsh sites with lagged responses of days to weeks. Integrating these asynchronous environmental and biological causal relationships in predictive models significantly improved model performance. More importantly, modeled CH4 emissions differed by up to a factor of 4 under a +1°C warming scenario when causality constraints were considered. These results highlight the significant role of causality in modeling wetland CH4 emissions especially under future warming conditions, while traditional data-driven ML models may reproduce observations for the wrong reasons. Our proposed causality-guided model could benefit predictive modeling, large-scale upscaling, data gap-filling, and surrogate modeling of wetland CH4 emissions within earth system land models.

Co-limitation towards lower latitudes shapes global forest diversity gradients

Liang, J., Gamarra, J.G.P., Picard, N. et al. Co-limitation towards lower latitudes shapes global forest diversity gradients. Nat Ecol Evol



Abstract: The latitudinal diversity gradient (LDG) is one of the most recognized global patterns of species richness exhibited across a wide range of taxa. Numerous hypotheses have been proposed in the past two centuries to explain LDG, but rigorous tests of the drivers of LDGs have been limited by a lack of high-quality global species richness data. Here we produce a high-resolution (0.025° × 0.025°) map of local tree species richness using a global forest inventory database with individual tree information and local biophysical characteristics from ~1.3 million sample plots. We then quantify drivers of local tree species richness patterns across latitudes. Generally, annual mean temperature was a dominant predictor of tree species richness, which is most consistent with the metabolic theory of biodiversity (MTB). However, MTB underestimated LDG in the tropics, where high species richness was also moderated by topographic, soil and anthropogenic factors operating at local scales. Given that local landscape variables operate synergistically with bioclimatic factors in shaping the global LDG pattern, we suggest that MTB be extended to account for co-limitation by subordinate drivers.

Non-destructive estimation of individual tree biomass: Allometric models, terrestrial and UAV laser scanning

Benjamin Brede, Loise Terryn, Nicolas Barbier, Harm M. Bartholomeus, Renée Bartolo, Kim Calders, Géraldine Derroire, Sruthi M. Krishna Moorthy, Alvaro Lau, Shaun R. Levick, Pasi Raumonen, Hans Verbeeck, Di Wang, Tim Whiteside, Jens van der Zee, Martin Herold;


Abstract: Calibration and validation of aboveground biomass (AGB) (AGB) products retrieved from satellite-borne sensors require accurate AGB estimates across hectare scales (1 to 100 ha). Recent studies recommend making use of non-destructive terrestrial laser scanning (TLS) based techniques for individual tree AGB estimation that provide unbiased AGB predictors. However, applying these techniques across large sites and landscapes remains logistically challenging. Unoccupied aerial vehicle laser scanning (UAV-LS) has the potential to address this through the collection of high density point clouds across many hectares, but estimation of individual tree AGB based on these data has been challenging so far, especially in dense tropical canopies. In this study, we investigated how TLS and UAV-LS can be used for this purpose by testing different modelling strategies with data availability and modelling framework requirements. The study included data from four forested sites across three biomes: temperate, wet tropical, and tropical savanna. At each site, coincident TLS and UAV-LS campaigns were conducted. Diameter at breast height (DBH) and tree height were estimated from TLS point clouds. Individual tree AGB was estimated for ≥170 trees per site based on TLS tree point clouds and quantitative structure modelling (QSM), and treated as the best available, non-destructive estimate of AGB in the absence of direct, destructive measurements. Individual trees were automatically segmented from the UAV-LS point clouds using a shortest-path algorithm on the full 3D point cloud. Predictions were evaluated in terms of individual tree root mean square error (RMSE) and population bias, the latter being the absolute difference between total tree sample population TLS QSM estimated AGB and predicted AGB. The application of global allometric scaling models (ASM) at local scale and across data modalities, i.e., field-inventory and light detection and ranging LiDAR metrics, resulted in individual tree prediction errors in the range of reported studies, but relatively high population bias. The use of adjustment factors should be considered to translate between data modalities. When calibrating local models, DBH was confirmed as a strong predictor of AGB, and useful when scaling AGB estimates with field inventories. The combination of UAV-LS derived tree metrics with non-parametric modelling generally produced high individual tree RMSE, but very low population bias of ≤5% across sites starting from 55 training samples. UAV-LS has the potential to scale AGB estimates across hectares with reduced fieldwork time. Overall, this study contributes to the exploitation of TLS and UAV-LS for hectare scale, non-destructive AGB estimation relevant for the calibration and validation of space-borne missions targeting AGB

On July 18th, together with all partners, we are finally kicking off our Open-Earth-Monitor project! This project embraces the use of open-source environmental data (remotely sensed and in-situ) and the development of methods and tools to enhance monitoring and assessments in critical regions on Earth. An open-source cyber-infrastructure will be built to significantly accelerate the uptake of environmental information, and to help build user communities at European and global levels. Thus, this project directly supports the open science goals of the GFZ.

To mark the project launch, a first public workshop with interactive debates, feedback rounds and demo sessions will take place on 19th July. Prominent representatives from the fields of Earth observation, data policy, business and science will be present. Please register here to attend the digital workshop for free:

The Open-Earth-Monitor project is coordinated by the OpenGeoHub Foundation, an independent non-profit research foundation promoting Open Source and Open Data solutions.


In recent decades, the world has experienced rapid growth in Earth Observation technology, which is considered by many to be one of the key tools to tackle environmental and climatic crises across borders. But this has come at a cost: Massive and inconsistent data volumes produced by EO sensors and ground monitoring networks are now overwhelming research networks; environmental information is often heavily under-used because it requires a high level of expertise and computing capacity. Therefore the targeted use of environmental data and other digital solutions is still rare among landholders, farmers, ecosystem regeneration practitioners, institutions and policy-makers, amongst other.

Expected impacts:

The Open-Earth-Monitor consortium consists of 23 partner organizations within and outside of Europe. The main goals of the consortium are to:

  • Produce an inventory of user needs, data and knowledge that will be used to develop a framework for increasing uptake and accessibility of environmental observation information;
  • Achieve permanent improvement in access to existing European and global environmental observation data for European stakeholders and make information more pertinent by reducing data complexity and increasing accessibility;
  • Develop a suite of intuitive tools to enable targeted end-users to monitor the status of natural resources at European and global scales, and production of environmental Business-2-Business solutions;
  • Built a comprehensive and systematic platform to enhance the FAIRness (Findability, Accessibility, Interoperability and Reusability) of environmental data by implementing the values of the European AI act and European GDPR Act;

Run an operational solution for processing and serving Earth Observation data, in-situ environmental data, Artificial Intelligence, Machine Learning and HPC models (OEMC-computing-engine).

Role of the GFZ

GFZ has a number of key roles in the Open-Earth-Monitor project:

  • The GFZ is co-leading the work on user-driven system design and FAIR data workflows and has an important role in the assessment of stakeholder engagement and user needs.
  • Another major task is to develop biomass estimation tools and to compile and process existing biomass data at European and global levels. Contained therein are the assessment and processing of available biomass datasets (space-based, in-situ) and the implementation of an open source tool to combine forest biomass estimates from different EO and ground-based data sources.
  • Furthermore, the GFZ will be leading the combination of different data sources for the development of a global forest GHG emissions monitor that can also be linked to different commodities and land use systemsrelated to oil palm, tropical timber, soy, beef and cacao.

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