Evaluating derived vegetation indices and cover fraction to estimate rangeland aboveground biomass in semi-arid environments

Abstract

This study was conducted to assess satellite data for quantifying and mapping the spatial distribution of rangeland biophysical parameters (aboveground biomass) from different geographic locations in the North West province, South Africa. Two major factors affecting the quality and conditions of the rangelands, namely rainfall zones and grazing intensities, were used to define sampling classes to represent the predominantly Savanah environment. Remote sensing vegetation indices (NDVI and SAVI) and vegetation cover fraction (SMA) were used to quantify the aboveground biomass using regression models of the sample aboveground biomass as a function. Results showed that private ranches in high rainfall areas yielded the highest above ground biomass (159 kg 100 m^-2) while the lowest biomass yield (10 kg 100 m^-2) was obtained from the communal rangelands in the low rainfall area. The SAVI performed well (0.64) in the low rainfall areas but the coefficient of determination between the AGB and SAVI was not significant (at p ≤ 0.05). The SMA also performed better than the NDVI (0.53) in the low rainfall areas but because of its weaknesses (0.57 and 0.48) in the high and medium rainfall areas, respectively, this tool is not ideal for quantifying AGB in the North West Province. In spite of its weakness in the low rainfall areas (0.47), the NDVI had displayed stronger coefficient of determination (0.63 and 0.70) with the AGB in the medium and high rainfall zones respectively. From a management perspective, remote sensing techniques appears to be a good alternative for managing and monitoring spatial and temporal AGB production, which is a major determinant for rangeland quality assessment