Reviewing the quality of global environmental maps

Prof. Edzer Pebesma and Prof. Hanna Meyer discuss the different variables and indicators on a digital global environment map. WWU - Michael Möller It could be so simple: producing global maps for vegetation, climate or soil at the touch of a button. Whether in Africa, America or Europe; whether up in the mountains or deep in the forest. No laborious on-site fieldwork would be necessary, nor would days spent evaluating data in a lab. Simply "train" the computer system to provide, as accurately as possible, predictions for any and every environmental variable. "Over the past few years, machine learning algorithms have become the most popular tool for modelling as they are able to identify non-linear, complex correlations," explains Prof. Hanna Meyer from the Institute of Landscape Ecology at the University of Münster. Well-known examples include the worldwide potential for restoring tree populations or the status of species of plants on the so-called Red List.