Utilising GIS and remote sensing to assess the impacts of the invasive Rubus cuneifolius on veld grazing capacity.

Abstract

This study was motivated by the heavily invasion of Rubus cuneifolius (bramble) in the Mistbelt grasslands of KwaZulu-Natal province of South Africa, particularly at Wakefield Farm. When not effectively managed, bramble invasion results in dire consequences, including the reduction of veld grazing capacity. The initial steps in managing invasive alien plants (IAPs) in rangelands involves understanding their distribution and extent. This requires a suitable satellite data with optimal temporal, spectral and spatial resolution, a task that necessitates accurate and feasible mapping of IAPs. In this regard, this study aimed to assess the utility of Sentinel-2 multispectral imager in mapping the spatial distribution of bramble and assessing its impact on veld grazing capacity. This overarching aim was addressed using two specific objectives. The first objective was to test the capabilities Sentinel-2 Multispectral Imager (MSI) in detecting and mapping bramble during the senescence period. To address this objective, four sets of spectral features (all spectral bands, mNDVI, mSR and combined inputs) and the Discriminant Analysis algorithm were used to test the utility of Sentinel-2 MSI’s in detecting bramble during the senescing stage. Inputs were tested when red edge bands were included in the analysis (inclusive bands) compared to when they were excluded from the analysis (exclusive bands). The second objective was to assess the impacts of bramble invasions on grass production, species diversity and dominance. To address this objective, grass biomass and species data we gathered and dry weight rank (DWR) and double sampling techniques were utilised. A relationship between the estimated biomass and the actual biomass was determined in the invaded and the uninvaded patches. Then, Shannon-Wiener diversity index and the Simpson’s Index were used to calculate species diversity and dominance, respectively. Results showed that bramble could be detected using Sentinel-2 MSI to an overall accuracy of 89.33% with red edge derived mNDVI being the most influential discrimination variable. Furthermore, results showed a significant relationship between the estimated and the actual biomass as well as a higher total biomass in the invaded patches. In uninvaded patches, species diversity was higher while dominance was lower and in the invaded patches species diversity was lower while dominance was higher. This study highlights that Sentinel-2 MSI's red edge bands are well-suited for discriminating invasive alien plants, particularly bramble, in rangelands during annual senescence. Additionally, it emphasizes that bramble invasion diminishes the value of rangelands by reducing the productivity of palatable grass species.