학술논문

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
Periodical
Source
IEEE Systems Journal Systems Journal, IEEE. 10(3):1282-1290 Sep, 2016
Subject
Components, Circuits, Devices and Systems
Computing and Processing
Satellites
Monitoring
Tides
Sea measurements
MODIS
Data models
Cyberspace
data fusion
geographic information sciences
Google Earth (GE)
harmful algal blooms (HABs)
prediction
remote sensing
Language
ISSN
1932-8184
1937-9234
2373-7816
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.