Assessing the synergistic potential of Sentinel-2 spectral reflectance bands and derived vegetation indices for detecting and mapping invasive alien plant species

Grassland biomes are valuable socio-economic and ecological resources. However, the invasion of grasslands by alien plant species has emerged as one of the biggest threats to their sustainability, management and conservation. Timely, cost-effective and accurate determination of invasive alien plant spatial distribution is paramount for mitigating the adverse effects of alien plants on natural grasslands. Whereas literature on use of optical bands for invasive alien plants detection and mapping is abound, there is paucity in literature on the integration of Vegetation Indices (VIs) and optical reflectance bands in invasive species mapping. Specifically, there is need to test the efficacy of improved and freely available sensors like Sentinel-2 in understanding landscape invasion. Hence, this study sought to assess the efficacy of Sentinel-2’s optical bands and VIs for improving the mapping of American Bramble (Rubus cuneifolius) within a grassland biome. Variable Importance in the Projection (VIP) was used to identify the most influential reflectance bands and VIs, which were then fused at a feature level to determine Bramble spatial distribution. To determine the optimal season for Bramble mapping, seasonal classification accuracies were executed in Support Vector Machine (SVM) learning algorithm and accuracies for Spring, Summer, Autumn and Winter seasons compared. Results show that although the highest overall accuracy was achieved using only optical bands, fused imagery increased overall classification accuracies during spring and autumn i.e. 70% to 73% and 63% to 65%, respectively. However, the fused imagery failed to improve on the benchmark of optical imagery during summer and winter. Findings from this study underline the efficacy of complementing VIs and optical bands in determining the distribution of invasive species within grasslands at specific seasons. Furthermore, this study advocates for the adoption and fusion of freely available new generation satellite imagery such as Sentinel-2 as a cost effective option in landscape mapping.