Name
Abstract | ||
|---|---|---|
Passive remote sensing is limited in that a two dimensional image is used to sense a three dimensional world. Multiple images over time add a fourth dimension, but time is under sampled with most remote sensing systems. Physical models of time varying environmental processes can be used to address the time and three dimensional aspect of the environment, but standalone models become inaccurate over time. Data assimilation is the term used to describe the continual input of new data into an executing model to keep the model aligned with reality. Some results and aspects of the Ensemble Kalman Filter data assimilation technique are described for two potential applications: water quality modeling and wildland fire modeling. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1109/AIPR.2008.4906472 | Washington DC |
Keywords | DocType | ISSN |
Kalman filters,data assimilation,fires,geophysics computing,remote sensing,water quality,ensemble Kalman filter,environmental models,passive remote sensing,remote sensing data assimilation,water quality modeling,wildland fire modeling | Conference | 1550-5219 E-ISBN : 978-1-4244-3126-7 |
ISBN | Citations | PageRank |
978-1-4244-3126-7 | 0 | 0.34 |
References | Authors | |
2 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Vodacek, A. | 1 | 1 | 0.71 |
| Li, Y. | 2 | 0 | 0.34 |
| Garrett, A.J. | 3 | 0 | 0.34 |