Rapidly advancing climate change has consequences for ecosystems and landscapes. Observing and understanding these changes is crucial for minimizing damage through smart action. Environmental monitoring that provides reliable data on the condition of forests, soils and peatlands – with as few gaps as possible – requires cooperative research which combines knowledge about ecological concepts with modern, computer-aided procedures, providing methods and technologies to extend specific date obtained from certain areas to larger scales: Machine learning techniques can be used to correlate data that foresters, landscape managers, and nature lovers find visible and measurable in the field with satellite and drone imagery of larger spatial areas. In theory, spatial and temporal gaps in the data could be closed using those methods – provided the mathematical models behind them are understood and the initial data with which the “artificial intelligence” learns actually matches the ecological question.
Hanna Meyer is Professor of Remote Sensing and Spatial Modeling at the Institute of Landscape Ecology at the University of Münster. In this episode of Digitalgespräch, the expert explains how environmental computer scientists work and what typical tasks and questions are. She describes what data is needed to correlate satellite and drone imagery with real ecological systems and how machine learning helps to fill gaps in the data. With hosts Marlene Görger and Petra Gehring, Meyer discusses what the limits of this mathematical extension of field data are – and the dangers of trusting the models too blindly.
Link to the article “Qualität globaler Umweltkarten auf dem Prüfstand” in wissen|leben (WWU Münster): https://www.uni-muenster.de/news/view.php?cmdid=12772
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The podcast is in German. At the moment there is no English version or transcript available.