ABSTRACT
Wild pollinator numbers are known to be positively associated with amounts of flower-rich habitat at landscape level. Increasing floral resources can be particularly beneficial in relatively nectar-poor agricultural systems and having a baseline understanding of the temporal and spatial availability of resources can allow targeted habitat management. Very high-resolution remote sensing has potential to facilitate accurate mapping of fine-scale, within-habitat pollinator foraging resources, thereby allowing spatial and temporal gaps to be identified and addressed, improving predictions of pollinator numbers, and enabling remote monitoring of pollinator conservation measures. Concentrating on hedgerow and flower-rich field margins in a UK agricultural landscape, we showed that multispectral airborne imagery with 3 cm and 7 cm spatial resolutions can be used to classify five nectar-rich flowering plant species (Prunus spinosa, Crataegus monogyna, Rubus fruticosus, Silene dioica and Centaurea nigra) using a maximum likelihood classification algorithm. In 2019, we separately acquired 3 cm and 7 cm imagery for the months of March, May and July, respectively. Overall accuracies were above 90% for each month at both 3 cm and 7 cm resolutions (range 92.32%-98.72%), supporting previous research that suggests higher spatial resolutions do not necessarily lead to higher accuracies, as pixel variability is increased. Remaining challenges include determining which co-flowering species of similar colours in the visible range can be distinguished from one another within classifications and quantifying floral unit density from classifications so that the nectar sugar supply can be calculated. Nonetheless, we provided a prototype approach for mapping pollinator foraging resources in an agricultural context, which can be extended to other nectar-rich species. The foundation is set for developing a remote sensing pipeline that can provide valuable data on the availability of nectar-rich flowering plant species at different time-points throughout the year.