Analysing uncertainty in multibeam bathymetry data and the impact on derived seafloor attributes  

Authors / CoAuthors

Lucieer, V.L. | Huang, Z. | Siwabessy, P.J.W.

Abstract

Seafloor bathymetric data and its derivatives fulfil a range of applications that are relevant to supporting the management of marine ecosystems and can provide a potentially powerful physical surrogate for benthic biodiversity. Similarly, morphological and seafloor terrain variables such as slope, curvature and rugosity derived from bathymetry data through GIS analysis not only describe seabed morphology but can also act as proxies for oceanographic processes The distributions of benthic marine fauna and flora most commonly respond to local changes in the topography of the seafloor. When seafloor topography is coupled with biological surveys it can help managers understand which environments contribute most to the growth, reproduction and survival of marine species. These models of habitat suitability provide natural resource managers with a tool with which to visualise the potential habitats of particular species. The accuracy of the habitat suitability models however, is critically reliant on the accuracy of underlying bathymetric data. The uncertainty in the bathymetric data is often ignored and often there is little recognition that the input bathymetric data and the derived spatial data products of the bathymetric data are merely modelled representations of one reality. These models can contain significant levels of uncertainty that are dependent upon the original depth measurements. This research paper explores a method to represent the uncertainty in bathymetric data. We discover that multibeam bathymetry data uncertainties are stochastic at individual soundings but exhibit a distinct spatial distribution with increasing magnitude from nadir to outer beams. We find that the restricted spatial randomness method is able to realistically simulate both the stochastic and spatial characteristics of the data uncertainty. This research concludes that the Monte Carlo method is appropriate for the uncertainty analysis of GIS operations and although the multibeam bathymetry data have notable overall uncertainty level, its impact on subsequent derivative analysis is likely to be minor in this dataset at the 2 m scale. Monitoring and change detection of the seafloor requires detailed baseline data with uncertainty estimates to ensure that features that display change are reliably detected. The accuracy of marine habitat maps and their associated levels of uncertainty are extremely hard to convey visually or to quantify with existing methodologies. The new techniques developed in this research integrate existing statistical techniques in a novel way to improve insights into classification and related uncertainty for seabed habitat maps which will progress and improve resource management for regional and national ocean policy.

Product Type

nonGeographicDataset

eCat Id

82201

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Keywords

• External PublicationArticle

( Theme )

• bathymetry

( Theme )

• NERP Marine Biodiversity Hub

( Theme )

• marine

Australian and New Zealand Standard Research Classification (ANZSRC)

• Marine Geoscience

• Published_Internal

Publication Date

2014-01-01T00:00:00

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Australian Government Security ClassificationSystem    

Classification - unclassified

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Creative Commons Attribution 4.0 International Licence    

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Use - license

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notPlanned

Topic Category

oceans

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This paper was the result of collaboration between IMAS of UTAS and Geoscience Australia within the NERP Marine Biodiversity Hub program.

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Spatial Resolution

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NERP Marine Biodiversity Hub