As the dominant vegetation species of most British moorlands, Calluna vulgarisis the focus of much scientific interest. While multiple approaches for quantifying the life cycle of the species have been tested, few, if any, have been successful. Given that British moorland environments are internationally valuable in terms of biogeography and carbon storage, but also inherently vulnerable to change, robust long-term monitoring is urgently needed. This study aimed to develop an original methodology, using UAV (Unmanned Aerial Vehicle) aerial imagery to quantify both flowering and vegetation height at different C. vulgaris growth phases. The former has been previously investigated, although not using low-altitude UAV imagery. The latter has not been tested before. Flower coverage was successfully quantified using supervised pixel-based classification, with statistically significant differences found between percentage flower coverage at each growth phase. Findings from height analysis, using structure-from-motion point clouds, suggest that modelled vegetation height can also be used to distinguish between growth phases. These findings demonstrate that UAV imagery can improve data acquisition in field ecology. The novel approach developed here, based on flower coverage and vegetation height, has potential for use beyond the moorland environment, to improve understadning of spatial characteristics of ecological systems and processes.