Title

Developing a Smart Semantic Web with Linked Data and Models for Near-Real-Time Monitoring of Red Tides in the Eastern Gulf of Mexico

Document Type

Article

Publication Date

9-2016

Keywords

cyberspace, data fusion, geographic information sciences, google earth (ge), harmful algal blooms (habs), prediction, remote sensing

Digital Object Identifier (DOI)

https://doi.org/10.1109/JSYST.2015.2440782

Abstract

In recent decades, the technology used to detect and quantify harmful algal blooms (commonly known as red tides) and characterize their physicochemical environment has improved considerably. A remaining challenge is effective delivery of the information generated from these advances in a user-friendly way to a diverse group of stakeholders. Based on existing infrastructure, we establish a Web-based system for near-real-time tracking of red tides caused by the toxic dinoflagellate Karenia brevis, which annually threatens human and environmental health in the eastern Gulf of Mexico. The system integrates different data products through a custom-made Web interface. Specifically, three types of data products are fused: 1) near-real-time ocean color imagery tailored for red tide monitoring; 2) K. brevis cell abundance determined by sample analysis; and 3) ocean currents from a nested and validated numerical model. These products are integrated and made available to users in Keyhole Markup Language (KML) format, which can be navigated, interpreted, and overlaid with other products in Google Earth. This integration provides users with the current status of red tide occurrence (e.g., location, severity, and spatial extent) while presenting a simple way to estimate bloom trajectory, thus delivering an effective method for near-real-time tracking of red tides.

Was this content written or created while at USF?

Yes

Citation / Publisher Attribution

IEEE Systems Journal, v. 10, issue 3, p. 1282-1290

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