Schmidtlein, S. (corresponding author, firstname.lastname@example.org), Feilhauer, H. (email@example.com) & Faude, U. (firstname.lastname@example.org): Department of Geography, University of Bonn, Meckenheimer Allee 166, D-53115 Bonn, Germany. Tichý, L. (email@example.com): Department of Botany and Zoology, Masaryk University,Kotlářská 2, CZ-611 37 Brno, Czech Republic.
A brute-force approach to vegetation classification
Article first published online: 12 OCT 2010
© 2010 International Association for Vegetation Science
Journal of Vegetation Science
Volume 21, Issue 6, pages 1162–1171, December 2010
How to Cite
Schmidtlein, S., Tichý, L., Feilhauer, H. and Faude, U. (2010), A brute-force approach to vegetation classification. Journal of Vegetation Science, 21: 1162–1171. doi: 10.1111/j.1654-1103.2010.01221.x
Co-ordinating Editor: Janos Podani.
- Issue published online: 26 OCT 2010
- Article first published online: 12 OCT 2010
- Received 31 August 2009;, Accepted 8 September 2010.
- Cluster optimization;
- Indicator species;
- Isometric feature mapping;
- Vegetation databases
Aim: Introduction of a novel approach to the classification of vegetation data (species by plot matrices). This approach copes with a large amount of noise, groups irregularly shaped in attribute space and species turnover within groups.
Method: The proposed algorithm (Isopam) is based on the classification of ordination scores from isometric feature mapping. Ordination and classification are repeated in a search for either high overall fidelity of species to groups of sites, or high quantity and quality of indicator species for groups of sites. The classification is performed either as a hierarchical, divisive method or as non-hierarchical partitioning. In divisive clustering, resulting groups are subdivided until a stopping criterion is met. Isopam was tested on 20 real-world data sets. The resulting classifications were compared with solutions from eight widely used clustering algorithms.
Results: When looking at the significance of species fidelities to groups of sites, and at quantity and quality of indicator species, Isopam often achieved high ranks as compared with other algorithms.