Derivation of High-Resolution Bathymetry from Multispectral Satellite Imagery: A Comparison of Empirical and Optimisation Methods through Geographical Error Analysis

Remote Sensing

Sarah M. Hamylton²  John D. Hedley¹  Robin J. Beaman³  Stuart Phinn⁴  Chris Roelfsema⁴  Xiaofeng Li⁴ 

[1] Environmental Computer Science Ltd., Raymond Penny House, EX16 Tiverton, UK;;School of Earth and Environmental Sciences, University of Wollongong, 2522 Wollongong, AustraliaCollege of Science, Technology & Engineering, James Cook University, 4870 Cairns, Australia;;id="af1-remotesensing-07-15829">1School of Earth and Environmental Sciences, University of Wollongong, 2522 Wollongong, Austral

关键词: coral reef;    landscape;    WorldView-2;    water depth;    spatial error;    Great Barrier Reef;    Lizard Island;    Sykes Reef;   

DOI  :  10.3390/rs71215829

来源: mdpi

【 摘 要 】

The high importance of bathymetric character for many processes on reefs means that high-resolution bathymetric models are commonly needed by marine scientists and coastal managers. Empirical and optimisation methods provide two approaches for deriving bathymetry from multispectral satellite imagery, which have been refined and widely applied to coral reefs over the last decade. This paper compares these two approaches by means of a geographical error analysis for two sites on the Great Barrier Reef: Lizard Island (a continental island fringing reef) and Sykes Reef (a planar platform reef). The geographical distributions of model residuals (i.e., the difference between modelled and measured water depths) are mapped, and their spatial autocorrelation is calculated as a basis for comparing the performance of the bathymetric models. Comparisons reveal consistent geographical properties of errors arising from both models, including the tendency for positive residuals (i.e., an under-prediction of depth) in shallower areas and negative residuals in deeper areas (i.e., an over-prediction of depth) and the presence of spatial autocorrelation in model errors. A spatial error model is used to generate more reliable estimates of bathymetry by quantifying the spatial structure (autocorrelation) of model error and incorporating this into an improved regression model. Spatial error models improve bathymetric estimates derived from both methods.

【 授权许可】

CC BY   
© 2015 by the authors; licensee MDPI, Basel, Switzerland.