Ecological applications of imaging spectroscopy in alpine grasslands

Imaging spectroscopy enables measuring vegetation optical properties to predict vegetation characteristics that are important for a wide range of ecological applications. Our aim was to predict fresh aboveground biomass of heterogeneous alpine grasslands in two areas and at two ecological scales. Furthermore, we assessed model plausibility for an intensively studied alpine grassland site (plant community scale) featuring distinct biomass and ungulate grazing patterns. Biomass data were collected in 51 plots and combined with imaging spectroscopy data to establish simple ratio models. We analysed the predictive power and transferability of models developed in two areas (Val Trupchun, Il Fuorn) and at two ecological scales (regional, local). In a next step, we compared our results to the broadband normalised difference vegetation index (NDVI). Finally, we assessed the correlations between model predictions and plant biomass distribution at the plant community scale. The best local simple ratio models yielded a model fit of R2 = 0.60 and R2 = 0.30, respectively, the best regional model a fit of R2 = 0.44. NDVI model performance was weaker for the regional and one local area, but slightly better for the other local area. However, at the plant community scale only the local model showed a significant positive correlation (RS = 0.39) with the known biomass distribution. Further, predictive power decreased when models were transferred from one local area to another or from one ecological scale to another. Our study demonstrated that imaging spectroscopy is generally useful to predict aboveground plant biomass in alpine grasslands with distinct grazing patterns. Site-specific local models based on simple ratio indices performed better than the NDVI or regional models suggesting that standardised approaches might not be adequate, particularly in heterogeneous grasslands inhabited by large ungulates. We emphasise the importance of collecting ground reference data covering the expected range of productivity and plant species composition. Moreover, plant community scale data from a previous study proved to be extremely valuable to test model plausibility.

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