Co-ordinating Editor: S. Bartha.
Numerical reproduction of traditional classifications and automatic vegetation identification
Article first published online: 11 JUN 2009
© 2009 International Association for Vegetation Science
Journal of Vegetation Science
Volume 20, Issue 4, pages 620–628, August 2009
How to Cite
De Cáceres, M., Font, X., Vicente, P. and Oliva, F. (2009), Numerical reproduction of traditional classifications and automatic vegetation identification. Journal of Vegetation Science, 20: 620–628. doi: 10.1111/j.1654-1103.2009.01081.x
- Issue published online: 6 JUL 2009
- Article first published online: 11 JUN 2009
- Received 19 April 2008; Accepted 31 July 2008.
- Expert systems;
- Fuzzy sets;
- Phytosociological data;
- Possibilistic C-means;
Questions: Is it possible to develop an expert system to provide reliable automatic identifications of plant communities at the precision level of phytosociological associations? How can unreliable expert-based knowledge be discarded before applying supervised classification methods?
Material: We used 3677 relevés from Catalonia (Spain), belonging to eight orders of terrestrial vegetation. These relevés were classified by experts into 222 low-level units (associations or sub-associations).
Methods: We reproduced low-level, expert-defined vegetation units as independent fuzzy clusters using the Possibilistic C-means algorithm. Those relevés detected as transitional between vegetation types were excluded in order to maximize the number of units numerically reproduced. Cluster centroids were then considered static and used to perform supervised classifications of vegetation data. Finally, we evaluated the classifier's ability to correctly identify the unit of both typical (i.e. training) and transitional relevés.
Results: Only 166 out of 222 (75%) of the original units could be numerically reproduced. Almost all the unrecognized units were sub-associations. Among the original relevés, 61% were deemed transitional or untypical. Typical relevés were correctly identified 95% of the time, while the efficiency of the classifier for transitional data was only 64%. However, if the second classifier's choice was also considered, the rate of correct classification for transitional relevés was 80%.
Conclusions: Our approach stresses the transitional nature of relevé data obtained from vegetation databases. Relevé selection is justified in order to adequately represent the vegetation concepts associated with expert-defined units.