Estimation of green grass/herb biomass from airborne hyperspectral imagery using spectral indices and partial least squares regression

Abstract

The main objective was to determine whether partial least squares (PLS) regression improves grass/herb biomass estimation when compared with hyperspectral indices, that is normalised difference vegetation index (NDVI) and red-edge position (REP). To achieve this objective, fresh green grass/herb biomass and airborne images (HyMap) were collected in the Majella National Park, Italy in the summer of 2005. The predictive performances of hyperspectral indices and PLS regression models were then determined and compared using calibration (n=30) and test (n=12) data sets. The regression model derived from NDVI computed from bands at 740 and 771nm produced a lower standard error of prediction (SEP=264gm−2) on the test data compared with the standard NDVI involving bands at 665 and 801nm (SEP=331gm−2), but comparable results with REPs determined by various methods (SEP=261 to 295gm−2). PLS regression models based on original, derivative and continuum-removed spectra produced lower prediction errors (SEP=149 to 256gm−2) compared with NDVI and REP models. The lowest prediction error (SEP=149gm−2, 19% of mean) was obtained with PLS regression involving continuum-removed bands. In conclusion, PLS regression based on airborne hyperspectral imagery provides a better alternative to univariate regression involving hyperspectral indices for grass/herb biomass estimation in the Majella National Park.