This study presents an interdisciplinary approach to monitoring vulnerable Mediterranean coastal dune habitats by integrating robotic and artificial intelligence technologies into traditional monitoring methods. The unstructured nature of coastal dunes presents a challenge for monitoring activities, leading to expensive and time-consuming actions from the human operators. Our solution uses a quadruped robotic platform, ANYmal C, to navigate unstable, irregular, and rough terrain and to adapt to dune conditions. The effectiveness and efficiency of the proposed method are demonstrated through the autonomous replication of monitoring missions, which mirrors the criteria used by human operators to acquire data. The data collected can be used to draw conclusions about the conservation status of the habitats by estimating biomass and vegetation cover, and to train artificial intelligence algorithms to detect target species. When paired with traditional phytosociological surveys performed by botanists, this robotics-assisted protocol not only streamlines field operations but also enhances data acquisition, storage, and usage. This pilot framework demonstrates how legged robots can improve data acquisition efficiency and support human experts in habitat monitoring, increasing the frequency and consistency of field observations while maintaining expert oversight. The result is a proactive and scalable framework for monitoring Mediterranean dune habitats that integrates agile legged robots and artificial intelligence to assist and strengthen traditional ecological monitoring.