The local Moran I grid calculates local autocorrelation of the bathymetry grid. It indicates local heterogeneity. The large and positive values represent positive autocorrelation or clumped pattern; the large negative values represent negative autocorrelation or checkerboard pattern; the values close to zero represent random local pattern. The grid was created from the bathymetry grid of Darwin Harbour. Please see the metadata of the bathymetry grid for details (GeoCat no: 74915).

This dataset contains data collected on various domestic and international swath surveys in and around Australian waters.

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.

The dataset contains: ER-Mapper format grids files using 0.01 degree resolution, colour TIF format images shaded with sun angle and azimuth 45 degree, legend files to go with the images, source data density images, documentation.

The dataset contains: ER-Mapper format grids files using 0.01 degree resolution, colour TIF format images shaded with sun angle and azimuth 45 degree, legend files to go with the images, source data density images, documentation.

Total contribution of six recently discovered submerged coral reefs in northern Australia to Holocene neritic CaCO3, CO2, and C is assessed to address a gap in global budgets. CaCO3 production for the reef framework and inter-reefal deposits is 0.26-0.28 Mt which yields 2.36-2.72 x105 mol yr-1 over the mid- to late-Holocene (<10.5 kyr BP); the period in which the reefs have been active. Holocene CO2 and C production is 0.14-0.16 Mt and 0.06-0.07 Mt, yielding 3.23-3.71 and 5.32-6.12 x105 mol yr-1, respectively. Coral and coralline algae are the dominant sources of Holocene CaCO3 although foraminifers and molluscs are the dominant constituents of inter-reefal deposits. The total amount of Holocene neritic CaCO3 produced by the six submerged coral reefs is several orders of magnitude smaller than that calculated using accepted CaCO3 production values because of very low production, a 'give-up' growth history, and presumed significant dissolution and exports. Total global contribution of submerged reefs to Holocene neritic CaCO3 is estimated to be 0.26-0.62 Gt or 2.55-6.17 x108 mol yr-1, which yields 0.15-0.37 Gt CO2 (3.48-8.42 x108 mol yr-1) and 0.07-0.17 Gt C (5.74-13.99 x108 mol yr-1). Contributions from submerged coral reefs in Australia are estimated to be 0.05 Gt CaCO3 (0.48 x108 mol yr-1), 0.03 Gt CO2 (0.65 x108 mol yr-1), and 0.01 Gt C (1.08 x108 mol yr-1) for an emergent reef area of 47.9 x103 km2. The dilemma remains that the global area and CaCO3 mass of submerged coral reefs are currently unknown. It is inevitable that many more submerged coral reefs will be found. Our findings imply that submerged coral reefs are a small but fundamental source of Holocene neritic CaCO3, CO2, and C that is poorly-quantified for global budgets.

Pacific island countries face a tsunami threat that consists of a complex mix of tsunamis from local, regional and distant sources. Assessment of risk on these islands requires the ability to model tsunami inundation, and such modelling is complicated by the fact that they are often surrounded by shallow coral reef systems whose influence on tsunami propagation is poorly understood. These islands also suffer from a lack of both bathymetry and topography data of sufficient resolution to accurately model tsunami inundation. Geoscience Australia and the Pacific Islands Applied Geoscience Commission (SOPAC) have been developing a capacity for tsunami inundation modeling in support of risk assessment for Pacific islands that relies on remote sensing for nearshore bathymetric data coverage, including shallow reef platforms. This technique uses a physics-based modeling approach that estimates bathymetry from multispectral imagery, based on an optimisation driven per-pixel estimation of a set of environmental variables, including water column depth, from a semi-analytical expression of sub-surface remote sensing reflectance. Using this approach we have developed models for shallow bathymetry for off Nuku'alofa in Tongatapu and Gizo in the Solomon Islands, and merged these models with available swath bathymetry and global bathymetry data to produce bathymetry grids suitable for modelling tsunami inundation. We have attempted to validate these models against data for the 2006 Tonga (Mw=8.0) and 2007 Solomon Islands (MW=8.1) earthquakes, respectively.

The Queen Charlotte Fault (QCF) off western Canada is the northern equivalent to the San Andreas Pacific - America boundary. Geomorphology and surface processes associated with the QCF system have been revealed in unprecedented detail by recent seabed mapping surveys. The QCF bisects the continental shelf of British Columbia forming a fault-valley that is visible in multibeam sonar bathymetry data. The occurrence of the fault within a valley, and its association with what appear to be graben structures, suggest the fault may exhibit minor rifting (extension) as well as strike-slip motions in the region offshore from Haida Gwaii (Queen Charlotte Islands). Fault-valley formation, slumping and stranding of submarine canyon thalwegs are geomorphic expressions of QCF tectonism, illustrating the general applications of multibeam technology to marine geophysical research.

The data currently held for bathymetry has been extracted from the GEBCO (General Bathymetric Chart of the Oceans) produced by the Natural Environment Research Council (UK).

Geoscience Australia is supporting the exploration and development of offshore oil and gas resources and establishment of Australia's national representative system of marine protected areas through provision of spatial information about the physical and biological character of the seabed. Central to this approach is prediction of Australia's seabed biodiversity from spatially continuous data of physical seabed properties. However, information for these properties is usually collected at sparsely-distributed discrete locations, particularly in the deep ocean. Thus, methods for generating spatially continuous information from point samples become essential tools. Such methods are, however, often data- or even variable- specific and it is difficult to select an appropriate method for any given dataset. Improving the accuracy of these physical data for biodiversity prediction, by searching for the most robust spatial interpolation methods to predict physical seabed properties, is essential to better inform resource management practises. In this regard, we conducted a simulation experiment to compare the performance of statistical and mathematical methods for spatial interpolation using samples of seabed mud content across the Australian margin. Five factors that affect the accuracy of spatial interpolation were considered: 1) region; 2) statistical method; 3) sample density; 4) searching neighbourhood; and 5) sample stratification by geomorphic provinces. Bathymetry, distance-to-coast and slope were used as secondary variables. In this study, we only report the results of the comparison of 14 methods (37 sub-methods) using samples of seabed mud content with five levels of sample density across the southwest Australian margin. The results of the simulation experiment can be applied to spatial data modelling of various physical parameters in different disciplines and have application to a variety of resource management applications for Australia's marine region.