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The Institute of Landscape Systems Analysis contributes to research activities in all three ZALF Core Topics.
Developing algorithms and modules for process-based simulation models must be concurrent with an understanding of the respective processes. First comes the elucidation of processes through experimentation, which is usually performed at fellow ZALF institutes or by external partners in their respective discipline. System modelling, however, can indicate current process theories that do not match observed data and which should, for this reason, be revised and perhaps investigated from a different perspective. In this manner the Institute supports research activities surrounding carbon cycling in landscapes and forensic investigations in
Core Topic I..
As soon as the models are ready to use, they often are applied for the analysis of cause-and-effect chains and feedback relations, e.g. the reaction of an agro-ecosystem to climate change or to the change of land use. For such research activities in the framework of
Core Topic II the Institute provides its tools, but also its expertise in systems analysis, e.g. for the development of sustainable land-use systems through the use of scenarios. Furthermore, we use the dialogue with model users in
Core Topic III to enhance model-based decision support systems for various applied tasks. The shaping of socio-economic structures and rules in rural areas can also be viewed as dynamic systems and are therefore a thrilling new sphere of activity for LSA scientists.
Within the Institute of Landscape Systems Analysis, research is organised in three areas. We develop basics for
Simulation Methods and Systems Theory, investigate processes and integrated system responses in terrestrial ecosystems (Ecosystem Modelling) and utilize large multi-source datasets to drive models at larger scales (Model-Data Integration)).
Dr. Ralf Wieland
Developing simulation methods and systems theory is an original task of the Institute and a unique feature with respect to ZALF research. New methods and applications for fuzzy modelling and machine learning in environmental research have recently been generated, in which the Institute continues to occupy a position of international leadership and bridges areas of food security and resource efficiency. The Institute’ flagship methods platform, open-source SAMT2 was recently launched in its 2.0 version. The research area methodically supports activities in all three ZALF Core Topics.
Dr. Dr. habil. Kurt Christian Kersebaum
The research area places its own topics in ensemble modelling of crop models with a focus on crop rotation systems. Scientists engage in research projects and networks on the reduction of uncertainty in process-based simulation of agricultural yields, for yield gap analysis, for scaling methods using 1D process models, and for soil-plant-atmosphere interactions under climate change, using its own models HERMES and MONICA. The research area serves primarily the Core Topics “Landscape Functioning” and “Land-use Change and Impacts” with relevant contributions to securing global food supply and sustainably use natural resources.
Dr. Ralf Wieland (acting)
Model data integration will move remote sensing and processing of large amounts of data for directly driving the models into the focus of the Institute, strategically involving remote sensing expertise of neighbouring institutions. Improvement of image interpretation and classification using meta-information according to Bayes and the assimilation of area data into the simulation of process models at the landscape scale are primary goals of this work.