Objective: To present a non-classificatory technique of map representation of compositional patterns of vegetation as no two plant species assemblages are completely alike and gradations often occur. Variation is depicted as continuous fields instead of classes.
Location: Murnauer Moos, Bavaria. Methods: The study combined vegetation ecology and remote sensing methods. The gradual representation of compositional patterns was based on techniques of ordination and regression, instead of mapping class fractions. The floristic field data were collected in relevés and subjected to three-dimensional non-metric multidimensional scaling (NMS). The reflectance information corresponding to plots was gathered from remotely sensed imagery with a high spectral resolution. Reflectance values in numerous wavelengths were related to NMS axes scores by partial least squares regression analysis. The regression equations were applied to the imagery and yielded three grey-scale images, one for each ordination axis. These three images were transformed into a red, green, and blue colour map with a specific colour for each position in the ordination space. Similar colours corresponded to similar species compositions.
Results: Compositional variation was mapped accurately (R2= 0.79), using continuous fields. The results took account of various types of stand transitions and of heterogeneities within stands. The map representation featured relatively homogeneous stands and abrupt transitions between stands as well as within-stand heterogeneity and gradual transitions.
Conclusions: The use of NMS in combination with imaging spectroscopy proved to be an expedient approach for non-classificatory map representations of compositional patterns. Ordination is efficiently extended into the geographic domain. The approach in abandoning pre-defined plant communities is able to reconcile mapping practice and complex reality.