How to predict plant functional types using imaging spectroscopy: linking vegetation community traits, plant functional types and spectral response. Issue 1 (3rd October 2016) - Schweiger, Anna K.; Schütz, Martin; Risch, Anita C.; Kneubühler, Mathias; Haller, Rudolf; Schaepman, Michael E.
Summary: The comparable and integrated nature of plant functional types and advances in high‐spectral‐resolution remote sensing techniques (i.e. imaging spectroscopy) make their combination highly interesting for spatially continuous and repeatable large‐scale ecosystem monitoring. Depending on physical environment and stress, plants invest in covarying biochemical and structural traits, influencing spectral characteristics of vegetation. These traits are assumed to bear a more direct causal relationship to plant functional types than to plant life/growth forms. However, the connection between a vegetation community's functional and spectral response remains to be established. We assessed the correlation structure between (i) biochemical and structural vegetation traits (biomass, dry matter content, nitrogen content, neutral detergent fibre content), (ii) plant life/growth forms and (iii) seven plant functional types of two categories (strategy types, indicator values) collected in heterogeneous alpine grassland. We then used airborne imaging spectroscopy data from the same area to model and predict plant life/growth forms and plant functional types at the vegetation community level using partial least squares regression and validated our models based on an independent data set. We found high correlations between many of the biochemical and structural vegetation traits, plant life/growth forms and plant functional types tested. Using airborne imaging spectroscopy data, we successfully modelled and predicted most plant life/growth forms ( R 2 max. = 0·56) and all plant functional types ( R 2 max. = 0·62). However, model performance for plant life/growth forms decreased substantially during external validation and overall model consistency was low (average change in R 2  = 72%), while plant functional type models were much more consistent (average change in R 2  = 20%). Based on our findings, we developed a conceptual framework using the theory and methodology of vegetation ecology and imaging spectroscopy to link the vegetation community's functional to its spectral signature. Our results encourage the use of plant functional types in imaging spectroscopy in order to aid the large‐scale monitoring of ecosystems, which is particularly important given the increased availability of airborne data and the prospective launches of spaceborne instruments in the near future.