Spatial prediction of coastal bathymetry based on multispectral satellite imagery and multibeam data

A - Papers appearing in refereed journals

Monteys, X., Harris, P., Caloca, S. and Cahalane, C. 2015. Spatial prediction of coastal bathymetry based on multispectral satellite imagery and multibeam data. Remote Sensing. 7 (10), pp. 13782-13806.

AuthorsMonteys, X., Harris, P., Caloca, S. and Cahalane, C.

The coastal shallow water zone can be a challenging and costly environment in which to acquire bathymetry and other oceanographic data using traditional survey methods. Much of the coastal shallow water zone worldwide remains unmapped using recent techniques and is, therefore, poorly understood. Optical satellite imagery is proving to be a useful tool in predicting water depth in coastal zones, particularly in conjunction with other standard datasets, though its quality and accuracy remains largely unconstrained. A common challenge in any prediction study is to choose a small but representative group of predictors, one of which can be determined as the best. In this respect, exploratory analyses are used to guide the make-up of this group, where we choose to compare a basic non-spatial model versus four spatial alternatives, each catering for a variety of spatial effects. Using one instance of RapidEye satellite imagery, we show that all four spatial models show better adjustments than the non-spatial model in the water depth predictions, with the best predictor yielding a correlation coefficient of actual versus predicted at 0.985. All five predictors also factor in the influence of bottom type in explaining water depth variation. However, the prediction ranges are too large to be used in high accuracy bathymetry products such as navigation charts; nevertheless, they are considered beneficial in a variety of other applications in sensitive disciplines such as environmental monitoring, seabed mapping, or coastal zone management.

Keywordsmultispectral; RapidEye; satellite; bathymetry; kriging; GWR
Year of Publication2015
JournalRemote Sensing
Journal citation7 (10), pp. 13782-13806
Digital Object Identifier (DOI)
Open accessPublished as ‘gold’ (paid) open access
FunderBiotechnology and Biological Sciences Research Council
Funder project or codeThe North Wyke Farm Platform [2012-2017]
Publisher's version
Output statusPublished
Publication dates
Online21 Oct 2015
Publication process dates
Accepted13 Oct 2015
Copyright licenseCC BY

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