Mapping of continuous floristic gradients in grasslands using hyperspectral imagery

摘要

Transitions between plant species assemblages are often continuous with the form of the transition dependent on the 'slope' of environmental gradients and on the style of self-organization in vegetation. Image segmentation can present misleading or even erroneous results if applied to continuous spatial changes in vegetation. Even methods that allow for multiple-class memberships of pixels presuppose the existence of ideal types of species assemblages that constitute mixtures-an assumption that does not fit the case of continua where any section of a gradient is as 'pure' as any other section like in modulations of grassland species composition. Thus, we attempted to spatially model floristic gradients in Bavarian meadows by extrapolating axes of an unconstrained ordination of species data. The models were based on high-resolution hyperspectral airborne imagery. We further modelled the distribution of plant functional response types (Ellenberg indicator values) and the cover values of selected species. The models were made with partial least squares (PLS) regression analyses. The realistic utility of the regression models was evaluated by full leave-one-out cross-validation. The modelled floristic gradients showed a considerable agreement with ground-based observations of floristic gradients (R{sup}2 = 0.71 and 0.66 for the first two axes of ordination). Apart from mapping the most important continuous floristic differences, we mapped gradients in the appearance of plant functional response groups as represented by averaged Ellenberg indicator values for soil pH (R{sup}2 = 0.76), water supply (R{sup}2 = 0.66) and nutrient supply (R{sup}2 = 0.75), while models for the cover of single species were weak. Compared to many other vegetation attributes, plant species composition is difficult to detect with remote sensing techniques. This is partly caused by a lack of compatibility between methods of vegetation ecology and remote sensing. We believe that the present study has the potential to increase compatibility as neither spectral nor vegetation information gets lost by a classifying step.