Our aim here is to develop and implement new methods of digital landscape analysis. One main focus is on landscape classification based on high-resolution remote sensing data. Important tools required for this are high-end software packages, such as ArcInfo, Erdas Imagine and eCognition. Processing chains for knowledge-based, automated high-precision and current landscape classification can then be developed and applied.
Our range of activities includes:
automated landscape classification based on high-resolution remote sensing data and aerial images
3D modelling based on high-resolution terrain models
application and development of geo-referenced simulation models for the probabilistic exposure and risk assessment of plant protection products
development of user-specific applications
evaluation of the potential of "new" geodata, e.g. LIDAR data and sensor-aided remote sensing data
development of large-scale processing environments (hardware and software)
implementation of (geo) data processing at regional and national scales
analysis and linking of climate data
Here we aim to understand ecological processes in agricultural landscapes so as to contribute to the realistic risk assessment of cultivation practices. The aim of describing ecological processes in agricultural landscapes is to contribute towards the highly realistic risk assessment of cultivation measures and to develop integrated environmental management that makes ecological and economic sense.
In terrestrial ecotoxicology, we therefore focus on the effects of plant protection products on non-target organisms in the agroecosystem, and develop enhanced methods to detect and evaluate such effects. In the area of environmental monitoring, we focus primarily on taking an inventory of endangered species, beneficial organisms and regionally significant pests. Taking ecological factors into account, we aim to derive recommendations for cultivation measures that are as environmentally friendly and economical as possible.
Our range of activities includes:
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