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Remote Sens. 2015, 7(10), 13782-13806; doi:10.3390/rs71013782

Spatial Prediction of Coastal Bathymetry Based on Multispectral Satellite Imagery and Multibeam Data

1
Geological Survey of Ireland, Beggars Bush, Haddington Road, Dublin 4 D04 K7X4, Ireland
2
Sustainable Soil and Grassland Systems, Rothamsted Research, North Wyke, Okehampton, Devon EX20 2SB, UK
3
Irish Climate Analysis and Research Units (ICARUS), Maynooth University, Maynooth, Co. Kildare, Leinster W23 F2H6, Ireland
4
National Centre for Geocomputation, Iontas, Maynooth University, Maynooth, Co. Kildare, Leinster W23 F2H6, Ireland
*
Author to whom correspondence should be addressed.
Academic Editors: Xiaofeng Li, Magaly Koch and Prasad S. Thenkabail
Received: 20 August 2015 / Revised: 8 October 2015 / Accepted: 13 October 2015 / Published: 21 October 2015
View Full-Text   |   Download PDF [1599 KB, uploaded 21 October 2015]   |  

Abstract

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. View Full-Text
Keywords: multispectral; RapidEye; satellite; bathymetry; kriging; GWR multispectral; RapidEye; satellite; bathymetry; kriging; GWR
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Monteys, X.; Harris, P.; Caloca, S.; Cahalane, C. Spatial Prediction of Coastal Bathymetry Based on Multispectral Satellite Imagery and Multibeam Data. Remote Sens. 2015, 7, 13782-13806.

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