Paper Info
Title
Spatio-temporal visualization of air-sea CO2 flux and carbon budget using volume rendering.
Abstract
This paper presents a novel visualization method to show the spatio-temporal dynamics of carbon sinks and sources, and carbon fluxes in the ocean carbon cycle. The air-sea carbon budget and its process of accumulation are demonstrated in the spatial dimension, while the distribution pattern and variation of CO2 flux are expressed by color changes. In this way, we unite spatial and temporal characteristics of satellite data through visualization. A GPU-based direct volume rendering technique using half-angle slicing is adopted to dynamically visualize the released or absorbed CO2 gas with shadow effects. A data model is designed to generate four-dimensional (4D) data from satellite-derived air-sea CO2 flux products, and an out-of-core scheduling strategy is also proposed for on-the-fly rendering of time series of satellite data. The presented 4D visualization method is implemented on graphics cards with vertex, geometry and fragment shaders. It provides a visually realistic simulation and user interaction for real-time rendering. This approach has been integrated into the Information System of Ocean Satellite Monitoring for Air-sea CO2 Flux (IssCO2) for the research and assessment of air-sea CO2 flux in the China Seas. Spatio-temporal visualization of satellite-derived air-sea CO2 flux is proposed.Carbon budget and its process of accumulation are displayed while showing CO2 flux.Satellite-derived air-sea CO2 flux products are constructed as particle systems.Large data are loaded in parallel and on demand for real-time visualization.Texture-based volume rendering works efficiently by GPU acceleration.
Year
DOI
Venue
2015
10.1016/j.cageo.2015.01.004
Computers & Geosciences
Keywords
DocType
Volume
Volume rendering,GPU,Visualization,Air–sea CO2 flux
Journal
77
Issue
ISSN
Citations 
C
0098-3004
0
PageRank 
References 
Authors
0.34
0
5
Name
Order
Citations
PageRank
Zhenhong Du13116.98
Lei Fang200.34
Yan Bai345.76
Feng Zhang434.76
Ren-yi Liu5291.53