Discriminating indicator grass species for rangeland degradation assessment using hyperspectral data resampled to AISA Eagle resolution

Abstract

The development of techniques to estimate and map increaser grass species is critical for better understanding the condition of the rangeland and levels of rangeland degradation. This paper investigates whether canopy reflectance spectra, resampled to AISA Eagle resolution can discriminate among four increaser species representing different levels of rangeland degradation. Canopy spectral measurements were taken from the four indicator species: Hyparrhenia hirta (HH), Eragrostis curvula (EC), Sporobolus africanus (SA), and Aristida diffusa (AD). The random forest algorithm and a forward variable selection technique were used to identify optimal wavelengths for discriminating the species. Results revealed that the optimal number of wavelengths (n=8) that yielded the lowest OOB error (11.36%) in discriminating among the four increaser species are located in 966.7, 877.6, 691.9, 718.7, 902.7, 854.8, 674.1 and 703nm. These wavelengths are located in the visible, red-edge and near-infrared regions of the electromagnetic spectrum. The random forest algorithm can accurately discriminate species with an overall accuracy of 88.64% and a KHAT value of 0.85. The study demonstrated the possibility to upscale the method to airborne sensors such as AISA Eagle for mapping indicator species of rangeland degradation. A rotational grazing management plan should be considered as a way to create sustainable rangeland management in degraded areas.