Comparative Analysis of Single Bands, Vegetation Indices, and Their Combination in Predicting Grass Species Nitrogen in a Protected Mountainous Area

Abstract

The role of biodiversity in improving the primary productivity within terrestrial ecosystems is well documented. Each species in an ecosystem has a role to play in the overall productivity of an ecosystem. Grass species nitrogen (N) estimation is essential in rangelands, especially in rugged terrain such as mountainous regions. It is an indicator of forage quality, which has nutritional implications for grazing animals. This research sought to improve and test the predictability of grass N by applying a combination of remotely sensed spectral bands and vegetation indices as input. Recursive feature selection was used to select the optimal spectral bands and vegetation indices for predicting grass N. Subsequently, the selected vegetation indices and bands were used as input into the non-parametric random forest (RF) regression to predict grass N. The prediction of grass N improved slightly in the vegetation indices model (81%) compared to the bands model (80%), and the highest prediction was achieved by combining the two (85%). This research ascertains that including red-edge-based vegetation indices improves the prediction of grass N. S2 MSI remains the ideal remote sensing tool for estimating grass N because of its strategically positioned red-edge bands, which are highly correlated with chlorophyll content in plants.