🤖 AI Summary
This study evaluates the capability of DESIS spaceborne hyperspectral data to predict plant species richness across two representative habitats in southeastern Australia—Southern Tablelands and Snowy Mountains. We systematically compare three feature extraction methods (PCA, CCA, PLS) coupled with three regression models (KRR, GPR, RFR), employing two-fold cross-validation. Results indicate that spectral bands in the red-edge, red, and blue regions contribute most substantially to prediction performance. DESIS-derived estimates significantly outperform those from Sentinel-2: in Southern Tablelands, r = 0.76 and RMSE = 5.89; in Snowy Mountains, r = 0.68 and RMSE = 5.95. This work demonstrates the feasibility and superiority of spaceborne hyperspectral data for regional-scale remote sensing of plant diversity, providing both methodological guidance and empirical validation for biodiversity assessment using next-generation hyperspectral satellites.
📝 Abstract
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.
