Combining Isomap ordination and imaging spectroscopy to map continuous floristic gradients in a heterogeneous landscape - Feilhauer, Hannes; Faude, Ulrike; Schmidtlein, Sebastian (Remote Sensing of Environment, 2011)
Mapping requires a meaningful generalization of information. For vegetation maps, classification is frequently used to generalize the species composition of (semi-)natural plant assemblages. As an alternative to classification, ordination methods aim to extract major floristic gradients describing the prevailing compositional variation in a floristic data set as metric variables. This ability has been used previously to derive gradient maps of homogeneous landscapes that show plant species composition in continuous fields. In the present study, gradient mapping was used in a more heterogeneous landscape with intricate environmental gradients and higher variation in vegetation physiognomy. Since established ordination methods may have difficulties to cope with the highly variable plant species composition, we tested the novel method Isometric Feature Mapping (Isomap) against conventional methods (Detrended Correspondence Analysis and Nonmetric Multidimensional Scaling). The resulting floristic gradients were related to hyperspectral imagery (HyMap) using partial least squares regression (PLSR) and subsequently mapped. Prediction uncertainties are provided as additional map. Isomap was able to preserve 74% of the original variation inherent to the floristic data set in a three-dimensional solution. This was considerably more than the established techniques achieved. The PLSR models for the floristic gradients extracted with Isomap showed model fits ranging from R =0.59 to R =0.73 in calibration and from R =0.55 to R =0.69 in tenfold cross-validation. The resulting gradient map provides detailed information on compositional vegetation patterns. © 2011 Elsevier Inc.
Keywords: Detrended correspondence analysis; Hyperspectral; Isometric feature mapping; Nonmetric multidimensional scaling; Partial least squares regression; Remote sensing; Species composition; Uncertainty; Vegetation