nerc.ac.uk

A front-following algorithm for AVHRR SST imagery

Shaw, A.G.P.; Vennell, R.. 2000 A front-following algorithm for AVHRR SST imagery. Remote Sensing of Environment, 72 (3). 317-327. 10.1016/S0034-4257(99)00108-X

Full text not available from this repository.

Abstract/Summary

A Front-Following Algorithm provides a new approach to determining the position and characteristics of thermal oceanic fronts using Advanced Very High Resolution Radiometer (AVHRR) sea surface temperature (SST) imagery. This algorithm differs from standard line enhancement, threshold edge detection, or classical contour techniques. Instead it utilizes a hyperbolic tangent function in a surface-fitting technique to follow an oceanic front. It has the advantage of describing the characteristics of an oceanic thermal front (including mean SST, SST difference, width and gradient across the front) and extracting information on the position and characteristics of the front into parameter form. Thus, the algorithm has the added benefit of recording the changes in the characteristics of the thermal front as it tracks along the front. The algorithm was applied to AVHRR SST imagery on part of the Subtropical Front known as the Southland Front (SF) off the east coast of the South Island of New Zealand, where subantarctic surface waters and subtropical surface waters converge. The algorithm was tested on examples of the SF and also compared with a standard gradient operator. The results showed that the algorithm performed well when following the SF, with low standard errors of parameter estimates, good visual verification of tracking, and consistent standards of accepted data. The algorithm estimated gradients better than a gradient operator.

Item Type: Publication - Article
Digital Object Identifier (DOI): 10.1016/S0034-4257(99)00108-X
ISSN: 0034-4257
Date made live: 15 Sep 2006 +0 (UTC)
URI: https://nora.nerc.ac.uk/id/eprint/141489

Actions (login required)

View Item View Item

Document Downloads

Downloads for past 30 days

Downloads per month over past year

More statistics for this item...