On variable relations between vegetation patterns and canopy reflectance - Feilhauer, Hannes; Schmidtlein, Sebastian (Ecological Informatics, 2011)
Statistical relations between the species composition of plant assemblages and canopy reflectance are frequently employed in remote sensing for mapping vegetation at local scales. Reflectance is influenced by species composition but also affected by dynamics such as seasonal vegetation development or plant stress. Due to this variability in time and space, doubts are frequently raised with respect to the transferability of statistical relations in remote sensing of plant assemblages. Hence, this study addresses the stability of statistical relations between species composition and reflectance despite of spatiotemporally changing vegetation conditions. We established permanent plots at three temperate sites (nutrient-poor grassland, wet heath, and floodplain meadow). We measured canopy reflectance at multiple dates over the vegetation period using a field spectrometer with hyperspectral resolution. Simultaneously, plant species composition and other vegetation and surface parameters that may exert influence on reflectance were recorded. Species composition was statistically related to the corresponding reflectance data using ordination (Isometric Feature Mapping) and cross-validated regression models (Partial Least Squares Regression). Time series of model fits as well as regression coefficients were used to estimate the temporal stability of the models. Model fits were further compared to changes in vegetation conditions. Model residuals were tested for co-variable influences. Finally, we tested the transferability of the statistical relations in time. Results showed that species composition could be modeled with rather high accuracies (R in validation up to 0.78 and for only three measurements lower than 0.5), with the highest fits near the vegetation optimum (i.e., the date with maximum cover of photosynthetically active vegetation). The transferability in time varied with the vegetation type. Uncertainties in the models were strongly related to variable canopy height and to the occurrence of litter. Since such spatial heterogeneities may be a result of non-stationary processes, we conclude that statistical methods taking into account such effects may further improve the accuracy of vegetation mapping. © 2011 Elsevier B.V.
Keywords: Hyperspectral remote sensing; Isometric feature mapping; Multitemporal; Non-stationarity; Partial Least Squares Regression; Plant species composition