The NDVI and spectral decomposition for semi-arid vegetation abundance estimation (International Journal of Remote Sensing, 1998)
This paper describes the use of the Normalized Difference Vegetation Index (NDVI) and spectral decomposition techniques to estimate vegetation abundance using ground-based spectroradiometric data of semi-arid vegetation. It is found that the NDVI provides a better measure of the proportion of directly irradiated leaves within the field-of-view of the spectroradiometer than it does the Leaf Area Index or biomass. Moreover, it is found that spectral decomposition isolates a factor which is strongly influenced by spectral variation at the region of the red edge. This factor is highly correlated with the NDVI -(R2=0.91) and as such also provides a good estimate of the proportion of directly irradiated leaves. It is suggested that spectral decomposition techniques provide a unique framework in which to analyse the factors affecting the spectral response of vegetation.
doi: 10.1080/014311698214217

Assessment of biophysical vegetation properties through spectral decomposition techniques (Remote sensing of environment, 1996)
This article demonstrates the use of spectral decomposi- tion for analyzing the spectral response of different semi- arid vegetation species found throughout Mediterranean Europe. Using this technique, it is possible to decompose a spectral data set into a smaller number of significant factors that represent the key variables affecting vegeta- tion spectral response. The results presented here show how spectral decomposition can be used to determine the intrinsic number and identity of the significant factors affecting the multispectral response. For the dataset inves- tigated here, which comprises field spectra recorded over 1130 wavelengths, using a GER single field-of-view IRIS (SIRIS) spectroradiometer, it was found that a combina- tion of just four factors was responsible for the majority of spectral variance. Interpretation of these factors was carried out by graphical analysis, stepwise regeneration of the original spectra, and correlation with biophysical data. Considering the identity of these factors, it was found that the second most significant factor (factor 2) was strongly related to the proportion of directly irradi- ated green leaves within the field-of-view of the spectrora- diometer. In addition, it was found that the fourth most significant factor (factor 4) provided a good summary of the spectral response of the different samples in the region of strong chlorophyll absorption. This demonstrates the possibility of using spectral decomposition techniques, particularly in environments dominated by spectrally similar vegetation classes, to model the mixed spectral population as mixtures of fundamental biophysical pa- rameters rather than as mixtures of the classes themselves.