Feature level image fusion of optical imagery and Synthetic Aperture Radar (SAR) for invasive alien plant species detection and mapping

Abstract Invasive alien plant species are regarded as a major threat to among others socio-economic systems, global biodiversity and conservation initiatives. A reliable understanding of their spatial and temporal distribution is paramount for understanding their impact on co-existing landscapes and ecosystems. While traditional passive remote sensing methods have been successful in assessing invasion of such species, limiting factors such as cost, restricted coverage, image availability, terrain and inadequate resolutions hamper mapping and detection at large spatial extents. To date, the adoption of active remote sensing techniques as complimentary data to invasive alien plant mapping has been limited. In this study, we fuse two commonly used medium spatial and spectral resolution imagery (Sentinel-2 and Landsat 8) with active remote sensing data (Synthetic Aperture Rada imagery) in determining the optimal season for detecting and mapping the American Bramble (Rubus cuneifolius). Feature level image fusion was adopted to integrate passive and active remote sensing imagery and Support vector machine (SVM) supervised classification algorithm used to discriminate the American Bramble from surrounding native vegetation. Seasonal results showed that Sentinel-2 data, fused with SAR data generated the highest classification accuracy during summer (76%), while Landsat 8 imagery fused with SAR data performed best in winter (72%). These findings demonstrate that fusion of SAR with traditional optical imagery can be used to detect and map the American Bramble at a regional scale. We conclude that SAR data can be used synergistically with optical remote sensing to improve discrimination and mapping of the American Bramble.