Remote sensing of forage nutrients: Combining ecological and spectral absorption feature data

Forage quality in grassland-savanna ecosystems support high biomass of both wild ungulates and domestic livestock. Forage quality is however variable in both space and time. In this study findings from ecological and laboratory studies, focused on assessing forage quality, are combined to evaluate the feasibility of a remote sensing approach for predicting the spatial and temporal variations in forage quality. Spatially available ecological findings (ancillary data), and physically linked spectral data (absorption data) are evaluated in this study and combined to create models which predict forage quality (nitrogen, phosphorus and fibre concentrations) of grasses collected in the Kruger National Park, South Africa, and analysed in both dry and wet seasons. Models were developed using best subsets regression modelling. Ancillary data alone, could predict forage components, with a higher goodness of fit and predictive capability, than absorption data (Ancillary: R2 adj ¼ 0:42—0:74 compared with absorption: R2 adj ¼ 0:11—0:51, and lower RMSE values for each nutrient produced by the ancillary models). Plant species and soil classes were found to be ecological variables most frequently included in prediction models of ancillary data. Models in which both ancillary and absorption variables were included, had the highest predictive capabilities ( R2 adj ¼ 0:49—0:74 and lowest RMSE values) compared to models where data sources were derived from only one of the two groups. This research provides an important step in the process of creating biochemical models for mapping forage nutrients in savanna systems that can be generalised seasonally over large areas