Research

Our research focuses on the combination of remote sensing data and modelling methods to detect landscape-ecological patterns and processes, as well as on the necessary developments in methodology. This includes the following two subject areas:

  • Applied multi-scale remote sensing of landscapes to derive independent atmospheric and ecosystem-based enviromental data sets and as a basis for the analysis of ecosystem parameters and processes.
  • Development of methods in the context of spatial and spatio-temporal machine modelling strategies in remote sensing as the basis for interdisciplinary application to landscape-ecological questions.

Current research projects

 

 

 

  • Logo of the project Carbon4D
    © Hanna Meyer

    Carbon4D

    Carbon-4D: A landscape-scale model of soil organic carbon mineralization in space, depth, and time

    The mineralization of soil organic carbon (SOC) is a key component of the global carbon cycle, which regulates the balance between CO2 efflux and SOC sequestration. Patterns of SOC mineralization in space, depth, and time (4D) across a landscape, however, are still poorly understood. This is mainly because SOC mineralization rates are driven by a complexity of regulators, mechanisms, and their nonlinear interactions, that is beyond human perception. The aim of “Carbon4D” is the development of a data-driven 4D model of SOC mineralization on a landscape scale that accounts for highly complex and nonlinear relationships. To reach this aim, measurements of SOC mineralization rates and of their controlling factors (SOC stocks, soil moisture and temperature) are combined with multi-source remote sensing data and weather- and soil information in a machine learning approach. Based on the learned relationships, 4D predictions are made for a typical German low mountain landscape located in Central Hesse that serves as the test area for Carbon4D. On the basis of the 4D model, a detailed analysis of temporal, spatial, and vertical patterns of SOC mineralization rates and their controlling factors will be conducted. Hence, Carbon4D will provide a first approach of a near real-time monitoring framework of SOC mineralization rates and soil CO2 efflux in all 4 dimensions, which will provide new insights into patterns and controlling factors.

    Project lead: Hanna Meyer, Nele Meyer (University of Bayreuth)
    Team@ILOEK: Maiken Baumberger, Hanna Meyer
    Funding: DFG
    Term: 2021 - 2024

  • © Hanna Meyer

    Uebersat

    Spatio-temporal transferability of satellite-based AI-models

    AI methods are increasingly used in the context of satellite-based earth observation to generate spatiotemporal environmental information, for example for monitoring land use, biodiversity patterns or effects of climate change. AI models are usually trained on the basis of local field observations with the aim to make predictions for a larger area and/or a new time for which no reference data are available. However, the transferability of the models to new locations and/or new times is rarely questioned in current AI-applications and models are often applied to make predictions far beyond the geographic location of the training samples. Especially in heterogeneous landscapes the new locations might differ considerably in terms of their environmental characteristics from what has been observed in the training data. This is problematic, since machine learning algorithms can fit very complex relationships, but at the same time are weak at extrapolation. Predictions for new locations/times that differ in their characteristics from the training data must therefore be considered very uncertain, which calls for a method to assess the area of applicability of AI-models.
    In this project new methods for the analysis and improvement of the transferability of satellite-based AI models in space and time will be developed, with the aim to assess and increase the quality of earth observation products through the use of innovative AI techniques. The new methods will be integrated into cloud-based processing chains to optimise data-driven applications and deliver more reliable monitoring results.

    Project lead: Hanna Meyer, Edzer Pebesma (IfGI)
    Team@GEOI: Marvin Ludwig, Jonathan Bahlmann, Hanna Meyer, Edzer Pebesma
    Funding: BMWi
    Term: 2021 - 2023

  • UAV image of a peatland
    © Jan Lehmann

    ReVersal

    Restoring peatlands of the nemoral zone under conditions of varying water supply and quality

    Project lead: Subproject remote sensing: Jan Lehmann, Hanna Meyer
    Team@ILÖK: Laura Giese, Jan Lehmann, Hanna Meyer
    Funding: DFG - BiodivERsA (ERA-Net Cofunds)
    Term: 2022 - 2025

  • Logo of the project Geo1Copter
    © Geo1Copter

    Geo1Copter

    Geo1Copter - Use of Unmanned Aircraft Systems (UAS) for high-resolution remote sensing in landscape ecology applications

    Unmanned Aerial Systems (UAS) as highly flexible and low-cost sensor platform provide new opportunities of data acquisition for various environmental and geoscientific purposes (e.g. environmental monitoring, forestry, geobase data etc.). Their spatial and temporal versatility due to small size, low weight and little operating costs also makes these aerial platforms an attractive tool for monitoring and observation purposes.

    Project Lead: Jan Lehmann
    Team@ILÖK: Jan Lehmann, Hanna Meyer, Henning Schneidereit
    Term: ongoing
    Further information: To the project homepage

  • Satellite-based image
    © Maite Lezama

    Antarctica

    Antarctic Science Platform

    Project Lead: Antarctica New Zealand
    Team@ILOEK: Maite Lezama Valdes, Hanna Meyer (International Partner within the objectives „Ecological connectivity across scales“ and „Meteorology, Climatology and Aeolian transport")
    Term: 2019 - 2025
    Further information: To the project homepage

  • © Hanna Meyer und Natur 4.0

    Natur 4.0

    LOEWE-focus Natur 4.0
    Conservation monitoring through networked sensors as a basis for sustainable biodiversity protection and securing ecosystem functions.


    Nature conservation strategies require the observation and assessment of landscape. Expert surveys must make compromises between the degree of detail, spatial coverage and temporal repetition, which can only be resolved to a limited extent by using airborne or satellite-based remote sensing approaches. This restricts differentiated nature conservation planning and reaction possibilities.
    The aim of LOEWE's Nature4.0 project is to develop a prototype of Nature4.0, a modular environmental monitoring system for high-resolution observation of species, habitats and processes relevant to nature conservation. Natur4.0 is based on the combination of expert knowledge and networked remote sensing and environmental sensors, which are attached to stationary and mobile platforms such as trees, UAVs, moving robots and animals. Together with powerful data integration and data analysis methods, Nature4.0 enables the differentiated and effective observation of landscapes. The recorded time series also serve to develop early warning indicators. Nature4.0 is thus breaking new ground in the field of comprehensive environmental monitoring. It consolidates in situ investigations by experts and uses non-regular data collection with mobile platforms in a crowdsensing approach to model nature conservation information in the form of regular, small-scale differentiated raster maps.

    Project Lead: University of Marburg
    Funding: LOEWE
    Team@ILOEK: Hanna Meyer (Associated partner and Co-Applicant of the sub-project “Remote Sensing and Spatial prediction”)
    Term: 2019 - 2022
    More Information: To the project homepage [de]