Item

Abstract

Hyperspectral satellite remote sensing may considerably contribute to the mapping and monitoring of dryland plant communities in nature reserve areas. This is especially true in areas that are not otherwise accessible and are undergoing rapid changes due to land use conversion such as the nature reserve “Döberitzer Heide”. For the purpose of mapping and understanding dynamic vegetation structures within dryland habitats multidimensional vegetation samples were transformed into comprehensive data spaces, using non metric multidimensional scaling. The resulting topology of field samples, consisting of species abundance could be interpreted as the distribution of species similarities along environmental gradients. A spatially explicit prediction of these gradients could be achieved by generating deterministic models from a PLS1 regression between ordination axis metrics and spectral variables derived from field measurements and applying the resulting models to image spectra, recorded from the HyMap airborne scanner. A four dimensional color interpolation scheme enabled the visualization of variable transition zones in species composition. Furthermore spectral field measurements were used as input variables to a random Forest classification based on vegetation clusters within the resulting ordination space. The final decision tree, which is determined by an overall accuracy of 82 %, was applied to the same digital image data. The external validation as well as an interpretation of predicted vegetation structures indicates the potential of combining both methods to extract valuable additional information for the monitoring of dryland habitats.