  The diversity of terrestrial vascular plants plays a key role in maintaining
the stability and productivity of ecosystems. Monitoring species compositional
diversity across large spatial scales is challenging and time consuming. The
advanced spectral and spatial specification of the recently launched DESIS (the
DLR Earth Sensing Imaging Spectrometer) instrument provides a unique
opportunity to test the potential for monitoring plant species diversity with
spaceborne hyperspectral data. This study provides a quantitative assessment on
the ability of DESIS hyperspectral data for predicting plant species richness
in two different habitat types in southeast Australia. Spectral features were
first extracted from the DESIS spectra, then regressed against on-ground
estimates of plant species richness, with a two-fold cross validation scheme to
assess the predictive performance. We tested and compared the effectiveness of
Principal Component Analysis (PCA), Canonical Correlation Analysis (CCA), and
Partial Least Squares analysis (PLS) for feature extraction, and Kernel Ridge
Regression (KRR), Gaussian Process Regression (GPR), Random Forest Regression
(RFR) for species richness prediction. The best prediction results were r=0.76
and RMSE=5.89 for the Southern Tablelands region, and r=0.68 and RMSE=5.95 for
the Snowy Mountains region. Relative importance analysis for the DESIS spectral
bands showed that the red-edge, red, and blue spectral regions were more
important for predicting plant species richness than the green bands and the
near-infrared bands beyond red-edge. We also found that the DESIS hyperspectral
data performed better than Sentinel-2 multispectral data in the prediction of
plant species richness. Our results provide a quantitative reference for future
studies exploring the potential of spaceborne hyperspectral data for plant
biodiversity mapping.
