Optimizing spectral resolutions for the classification of C3 and C4 grass species, using wavelengths of known absorption features

Abstract

Abstract. Hyperspectral remote-sensing approaches are suitable for detection of the differences in 3-carbon ($C3$) and four carbon ($C4$) grass species phenology and composition. However, the application of hyperspectral sensors to vegetation has been hampered by high-dimensionality, spectral redundancy, and multicollinearity problems. In this experiment, resampling of hyperspectral data to wider wavelength intervals, around a few band-centers, sensitive to the biophysical and biochemical properties of $C3$ or $C4$ grass species is proposed. The approach accounts for an inherent property of vegetation spectral response: the asymmetrical nature of the inter-band correlations between a waveband and its shorter- and longer-wavelength neighbors. It involves constructing a curve of weighting threshold of correlation (Pearson’s $r$) between a chosen band-center and its neighbors, as a function of wavelength. In addition, data were resampled to some multispectral sensors—ASTER, GeoEye-1, IKONOS, QuickBird, RapidEye, SPOT 5, and WorldView-2 satellites—for comparative purposes, with the proposed method. The resulting datasets were analyzed, using the random forest algorithm. The proposed resampling method achieved improved classification accuracy ($κ=0.82$), compared to the resampled multispectral datasets ($κ=0.78$, 0.65, 0.62, 0.59, 0.65, 0.62, 0.76, respectively). Overall, results from this study demonstrated that spectral resolutions for $C3$ and $C4$ grasses can be optimized and controlled for high dimensionality and multicollinearity problems, yet yielding high classification accuracies. The findings also provide a sound basis for programming wavebands for future sensors.