Building a STEM research and education network of GIS and drone mapping for coastal seagrass monitoring

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Seagrass suffers from several threats, including wasting disease, that could impair ecosystems, and accurate and up-to-date mapping of seagrass is critical for understanding estuary health and resilience to environmental stressors. This project advances fundamental research using Unmanned Aerial Vehicles (UAVs) to map seagrass across Northern California to detect disease and analyze temporal trends in seagrass health and extent. A significant focus of this project is creating a STEM network between minority-serving institutions and community colleges to foster collaborations and build capacity, training and mentoring 24 students in 4-year university and 2-year community colleges annually, providing them with interdisciplinary skills such as UAV piloting, GIS, coastal science, and scientific communication. Students also develop professional networks and experience study, promoting long-term engagement in STEM. The goal is to extend this training program to community organizations, citizen scientists, and practitioners, aiming to contribute to coastal management and seagrass conservation. Efficient, comprehensive monitoring of the seascape-scale impact of seagrass disease is essential to predict future ecological impacts and implement early interventions. However, seagrass conditions are rarely quantified in spatial and temporal domains, and traditional mapping methods are labor-intensive, expensive, or have a coarse resolution. UAV imaging combined with GIS are emerging technologies for monitoring coastal seagrass ecosystems because of their spatial high-resolution, temporal flexibility, and cost-efficiency. Our project utilizes multi-platform UAV systems with visible, multispectral, and LiDAR sensors to provide more accurate data for better delineation and classification of seagrass and predicting disease more effectively. Combined with ground data and in situ sampling, we develop a UAV and GIS-based platform for comprehensive monitoring of seagrass ecosystems. The platform aid in the detection of diseases at an individual leaf scale in prominent seagrass habitats. Our research includes multi-platform UAV mapping, spatial modeling of the seagrass disease, temporal analysis of seagrass bed extent changes, development of AI-based image analysis for species classification and disease detection, creation of a cloud data hub for real-time data exchange, and provision of research training and professional development opportunities for students.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.