A neural solution: a data driven assessment of global climate and vegetation classes

Kohonen's self-organising map (SOM), combined with a measure of topological ordering, is applied to solve a complex classification problem. Climate classifications are mostly empirically-based and often mix the mutual impact between climate, soil and vegetation. Therefore, the influence of abiotic factors on the broad-scale vegetation distribution is of major interest. In order to assess this problem, a spatially highly-resolved climate and soil database is used as training data for a SOM. Inherent feature types hidden in the database are identified, leading to a global pattern of archetypal climatic and soil domains. Additionally, such a classification scheme can be used for comparison with vegetation models and allows a network-based estimation of the potential broad-scale distribution of ecosystem complexes.