Common Reed Phragmites australis, globally one of the mostly widely distributed wetland plants, is important for biodiversity and for humans. However, like most wetland plant communities, reedbed has rarely been mapped at large geographical scales, restricting the information available to study reed's range dynamics or inform its management. Using Sentinel-2 data and machine learning, we aimed to produce the first published remotely-sensed reedbed map of Britain; however, accuracy as assessed by field validation was relatively low (AUC = 0.671), with many false positives (commission error of 93.4%). A similar workflow carried out in Google Earth Engine, using nearly an order of magnitude more images, gave a lower commission error but a disproportionately higher omission error. Using the known commission and omission error, we estimate that in 2015-2017 ~ 7800 ha of Britain was reedbed. Our study highlights the enduring barriers to accurate land cover classification at large spatial scales. Even with a 'big data' approach, reflectance error and ecological factors such as confusion land cover types and geographical variation in temporal reflectance function will probably continue to limit the size of area for which land cover can be classified accurately, therefore limiting the utility of remote sensing for ecologists.
© 2024. The Author(s).