Title :
A neural solution: a data driven assessment of global climate and vegetation classes
Author_Institution :
Inst. of Chem., Oldenburg Univ., Germany
Abstract :
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
Keywords :
biology computing; botany; climatology; ecology; environmental science computing; geographic information systems; pattern classification; self-organising feature maps; soil; topology; visual databases; Kohonen self-organising map; abiotic factors; archetypal climatic domains; archetypal soil domains; broad-scale vegetation distribution; climate classification; data-driven assessment; ecosystem complexes; global climate classes; global pattern; inherent feature type identification; neural network-based estimation; spatially resolved database; topological ordering; training data; vegetation classes; Biological system modeling; Chemistry; Ecosystems; Meteorology; Soil measurements; Soil properties; Spatial resolution; Temperature; Training data; Vegetation mapping;
Conference_Titel :
Neural Information Processing, 1999. Proceedings. ICONIP '99. 6th International Conference on
Conference_Location :
Perth, WA
Print_ISBN :
0-7803-5871-6
DOI :
10.1109/ICONIP.1999.844000
