Ground-based robotic remote sensing for standardized biodiversity monitoring in coastal habitats

Autonomous remote-sensing technologies are increasingly contributing to biodiversity monitoring by enabling scalable, repeatable, and minimally invasive data collection. We present a ground-based robotic remote-sensing framework that integrates artificial intelligence and standardized quality assurance to support the derivation of decision-ready ecological indicators. Using European coastal dunes as a case study, we deployed an AI-enabled quadruped robot equipped with near-ground imaging sensors to monitor the host–herbivore interaction between Pancratium maritimum and Brithys crini. In this citizen-to-robot pipeline, expert-verified citizen-science imagery was used to train lightweight detection models for on-board inference and higher-capacity models for offline auditing, ensuring reproducibility and transparency across missions. Field trials demonstrated that the system achieved consistent image quality, accurate detections, and low-disturbance operation under natural conditions, capturing spatially explicit evidence of herbivory and host condition. By coupling standardized protocols with robotic autonomy, this approach implements a proximal remote-sensing layer that complements aerial and satellite observations. The workflow is designed to support transferable quantification of species interactions and habitat condition across sites and seasons, contributing to the integration of robotics and ecological remote sensing for biodiversity assessment and conservation management.