Peer-Reviewed Journal Details
Mandatory Fields
Monteys, X;Harris, P;Caloca, S;Cahalane, C
2015
October
Remote Sensing
Spatial Prediction of Coastal Bathymetry Based on Multispectral Satellite Imagery and Multibeam Data
Published
()
Optional Fields
SHALLOW-WATER BATHYMETRY VARIABLE BOTTOM TYPES INVERSION MODEL SUN GLINT DEPTH DERIVATION BAY INTERPOLATION INFORMATION LIMITATIONS
7
13782
13806
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.
BASEL
2072-4292
10.3390/rs71013782
Grant Details