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Working groups

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Contribution to ZALF research

A profound knowledge of the vast number of processes interacting in landscapes is a necessary but by no means sufficient prerequisite for both landscape research and the sustainable use of landscapes according to the UN Sustainable Development Goals. Due to common multi cause-multi effect relationships and the simultaneity of natural and anthropogenic processes, empirical approaches and methods are often fraught with high uncertainties. There is thus an urgent need for extensive and well-documented datasets as well as for sophisticated diagnostic tools as prerequisites for testing and refining our understanding of landscape functioning. The service groups within the Research Platform “Data” will ensure the comprehensive acquisition of metadata, the long-term storage of well-documented data, and the provisioning of user-friendly interfaces for data access, search and retrieval.

In addition, powerful tools for the analysis of large and heterogeneous datasets must be applied and continuously enhanced. Modern landscape research requires methods that explicitly account for the challenges of high-dimensional, heterogeneous (i.e. nominal, ordinal, interval and ratio measurement scales) datasets with different temporal and spatial coverage and their typical characteristics (e.g. non-linearity, non-stationarity, spatial correlation, and memory effects).

The activities of the Research Platform “Data” build upon the data collected in Research Areas 1 and 2 and the associated thematic expertise. They are also closely linked to the data-based modelling activities in the Research Platform “Models & Simulation” as well as the interdisciplinary analyses and the development of a landscape theory in Research Area 3.


Working groups


Dimensionality Assessment and Reduction

Image of the WG Data Dimensionality 

Despite the large number of potential observables, landscapes are regarded as highly constrained systems with rather low effective degrees of freedom. The working group will quantify the number of effective degrees of freedom of large environmental datasets (e.g. via correlation dimension) and subsequently determine the dominating processes using methods of dimensionality reduction (e.g. principal component analysis, isometric feature mapping, etc.). To this end, the working group will use methods that explicitly consider the challenges of high-dimensional, heterogeneous datasets with different temporal and spatial coverage and typical characteristics like non-linearity, non-stationarity, spatial correlation, and memory effects. To date, these methods are rarely applied in most disciplines of environmental or related socio-economic science.

Contact: Prof. Dr. Gunnar Lischeid


Small Water Bodies in Agricultural Landscapes

Image of the WG Small Water Bodies

The aim of the working group is to better understand the often complex interactions between hydrological, biological and biogeochemical processes and thus lay the foundations for protection and sustainable management, even under changing anthropogenic and climatic conditions. Too this end, it is necessary to systematically combine expertise and methods from different disciplines such as hydrology and hydrogeology, soil science, biology including microbiology, etc. The working group thus follows a strongly interdisciplinary research approach, as generally the aim of ZALF.

Contact: Prof. Dr. Gunnar Lischeid


Geodata (Service)

Image of the WG Geodata (Service)

It is the task of this service group to provide a common, high-quality, consistent and well-documented geodatabase for agricultural landscape research as part of the ZALF geodata infrastructure (GDI), accounting for different aggregation levels and in consideration of the highly variable previous knowledge of the data users regarding the handling of geodata. This includes both ZALF data as well as data from external sources.

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Contact: Dr. Uwe Heinrich


Research Data (Service)

Image of the WG Research Data (Service)

The working group delivers high-quality services for all ZALF researchers in all aspects of research data management and at all stages of the research process. Our key objective is the liberation of research potentials through an interoperable research data network with orchestrated workflows within and out of ZALF. This ensures the emerging of our ZALF “knowledge nexus” as a unique asset for every ZALF researcher. Important work results are ZALF Data Policy, ZALF Core Data Set, ZALF Framework, ZALF Repository and ZALF Data Management Plans. Our ZALF impact is catalyzed by our network activities and the brokering of new AI projects.

Contact: Adrian Josef Krolczyk


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