Applications of unmanned vehicle systems for multi-spatial scale monitoring and management of aquatic ecosystems: A review - Xingzhen Liu; Long Ho; Stijn Bruneel; Peter Goethals (Ecological Informatics)
Aquatic ecosystems are facing intensifying challenges, such as pollution, habitat destruction, and climate perturbations; hence, high-resolution, cost-effective monitoring tools are becoming increasingly important to provide timely and accurate data for effective conservation and management strategies. This review investigates the crucial role of unmanned vehicle systems (UVS) in conducting multi-spatial scale monitoring across different facets of aquatic ecosystems, including ecosystem habitat, algal bloom, vegetation conditions, and animal behaviors. Using a combination of bibliometric analysis and systematic review techniques, we assess UVS applications over the past decade (2013−2023), track research trends and evaluate the effectiveness, challenges, and prospects of UVS technologies. Our findings reveal a 12-fold increase in UVS applications in aquatic research during this period, with 60–70 % focusing on habitat monitoring and animal behavior research, and less than 10 % addressing algal blooms and eutrophication. Unmanned Aerial Vehicles (UAV), representing 70 % of the published applications, have been the primary research instrument, outpacing Unmanned Surface Vehicles (USV) and Autonomous Underwater Vehicles/Remotely Operated Vehicles (AUV/ROV). While enhancing monitoring at various scales with broad coverage, integrated applications between UAVs and USVs or AUVs/ROVs still need to solve crucial issues, such as weather impacts, communication complexities, and data processing needs. The systematic analysis highlights the gap in UVS applications for multi-spatial scale monitoring and reveals significant opportunities for integrating UVS with Artificial Intelligence (AI), machine learning, and Internet of Things (IoT) technologies, which are improving UVS integration, security, and efficiency, and enabling better resource management and navigation accuracy. Furthermore, object tracking, Digital Image Processing (DIP), Geographic Information System (GIS), and data management platforms enable efficient, multi-spatial scale monitoring and advanced research capabilities, offering the potential to enhance data collection and management of aquatic ecosystems.