Imaging spectroscopy and grass phosphorous concentration in an African savanna

Abstract

The remote sensing of foliar phosphorus has received very little attention as compared to nitrogen, yet it plays an equally critical role in explaining the distribution and feeding patterns of herbivores. Our objective was to map grass phosphorous concentration in a savanna rangeland using hyperspectral remote sensing. Band depths from two continuum-removed absorption features as well as the red edge position were input into a backpropagation neural network. Following a series of experiments to ascertain the optimum wavelengths, the best trained neural network was used to predict and ultimately to map grass phosphorous concentration in the Kruger National Park. Results indicate that, the best trained neural network could predict phosphorous distribution with a high accuracy. The study demonstrates the potential of imaging spectroscopy in mapping grass phosphorous concentration in savanna rangelands.