A systematic review of unmanned aerial vehicles (UAVs) for coastal ecosystem monitoring

Unmanned Aerial Vehicle (UAV) remote sensing has gained increasing attention in the scientific community and has rapidly evolved into a widely used tool for diverse applications, particularly in coastal environment monitoring. This study presents a comprehensive review of UAV-based coastal ecosystem monitoring by analysing 1972 research articles published between 2020 and 2024. Following the PRISMA framework, 406 articles were systematically selected, from which 100 studies underwent detailed technical and ecological analysis. The review critically evaluates UAV platforms, sensor technologies, ecological applications, spatial resolutions, analytical algorithms, field validation approaches, software tools, and observed limitations. The study further provides an in-depth discussion on the current status, emerging trends, and technological advancements in the field, along with recommendations and research directions. Key findings reveal that multirotor platforms with RGB cameras remain dominant, while there is a clear shift towards multispectral, hyperspectral, and LiDAR integration. Additionally, the standardisation of SfM-MVS photogrammetric workflows and the increasing use of RTK/PPK positioning systems are apparent, although GCP-based validation still remains common. The analytical landscape has evolved toward automated machine learning and deep learning frameworks, though weak model interpretability remains a persistent bottleneck. UAVs demonstrated clear advantages for fine-scale ecological mapping, event-driven monitoring, and surveys in inaccessible environments, while geometric accuracy assessment was consistently prioritised in the field validation. Emerging opportunities include sensor/model fusion, explainable AI integration, and new ecological applications such as carbon flux estimation. Hence, this review provides a comprehensive foundation for researchers to effectively integrate UAVs into coastal monitoring applications and identify future research directions.