The spectral diversity hypothesis proposes that as the number of plant species increases for a given area, the diversity of spectra observed from that area should also increase. This approach could be very useful as an assessment and monitoring tool to help ecologists understand the spatial and temporal patterns of biodiversity without relying on consistently detecting individual species. While the spectral diversity hypothesis has been examined for a wide range of ecosystems using a variety of remote sensing data, it has not been tested using spectroscopy (i.e. hyperspectral) data for wetlands. Previous studies have not explicitly considered the impact that flowers may have on spectral diversity and how this may impact the spectral diversity hypothesis. To test the spectral diversity hypothesis and the potential impact on flowers, we used a simulation approach to combine leaf and flower spectra collected from a diverse prairie fen wetland ecosystem into datasets of virtual plots with varying levels of species diversity and different combination of species. To address the high dimensionality of the data, we compared spectral diversity and floristic diversity using partial least squares regression. Our results found that defining floristic diversity using the Shannon's diversity index, which accounts for plant abundance in each plot, produced the best predictive models where the predicted values had a RMSE less than 40% of the mean observed value. We also found that the inclusion of flower spectra with leaf spectra did increase the RMSE of the best model, but across all models, correlation increased. Our results indicate that spectral diversity could be used as an initial biodiversity assessment tool for wetlands, especially with on-going advancements in unmanned aerial vehicle technology that can provide a low altitude platform for imaging spectroscopy.
|State||Published - Jan 1 2015|