The datasets measure the K490 parameter (Downwelling diffuse attenuation coefficient at 490 nm, a turbidity parameter) of Australian oceans. They are derived products from MODIS (aqua) images using NASA's SeaDAS image processing software. The extent of the datasets covers the entire Australian EEZ and surrounding waters (including the southern ocean). The spatial resolution of the datasets is 0.01 dd. The datasets contain 36 monthly k490 layers between 2009 and 2011. The unit of the datasets is 1/m.

Marine visual imaging has become a major assessment tool in the science, policy and public understanding of our seas and oceans. The technology to acquire and process this imagery has significantly evolved in recent years through the development of new camera platforms, camera types, lighting systems and analytical software. These advances have led to new challenges in imaging, including storage and management of `big data, manipulation of digital photos, and the extraction of biological and ecological data. The need to address these challenges, within and beyond the scientific community, is set to substantially increase in the near future, as imaging is increasingly used in the designation and evaluation of marine conservation areas, and for the assessment of environmental baselines and impact monitoring for maritime industry. We review the state of the theory, techniques and technologies associated with each of the steps of marine imaging for observation and research, and to provide an outlook on the future from this active scientific and engineering community that develops and uses it.

At the Australian Marine Sciences Association conference held in Canberra in July 2014, a group of scientists and managers engaged in a roundtable discussion to identify areas where linkages could be improved between researchers working on marine population connectivity and managers of marine areas. Population connectivity is the degree of demographic connectedness between populations, indicating the degree to which populations are linked through dispersal and recruitment of organisms, or through gene flow. Connectivity allows organisms and genes to move among different habitats, helping to ensure survival of species by providing increased habitat and reproduction options, and helping to maintain genetic variability. Although connectivity science was used as the focal point of the discussion, the issues discussed are applicable to other topics at the interface of science and management. Here we summarise the key themes and outcomes/recommendations from the discussion.

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.

A fully four-dimensional (3D x time) object-oriented biophysical dispersal model was developed to simulate the movement of marine larvae over semi-continuous surfaces. The model is capable of handling massive numbers of simulated larvae, can accommodate diverse life history patterns and distributions of characteristics, and saves point-level information to a relational database management system.

Geoscience Australia's GEOMACS model was utilised to produce hindcast hourly time series of continental shelf (~20 - 300 m depth) bed shear stress (unit of measure: Pascal, Pa) on a 0.1 degree grid covering the period March 1997 to February 2008 (inclusive). The hindcast data represents the combined contribution to the bed shear stress by waves, tides, wind and densitydriven circulation. Included in the parameters that represent the magnitude of the bulk of the data are the quartiles of the distribution; Q25, Q50 and Q75 (i.e. the values for which 25, 50 and 75 percent of the observations fall below). Q25, or the 0.25 Quartile of the Geomacs output, represents the values for which 25% of the observations fall below (Hughes & Harris 2008).

Understanding the distribution and abundance of sponges and their associated benthic habitats is of paramount importance for the establishment and monitoring of marine reserves. Benthic sleds or trawls can collect specimens for taxonomic and genetic research, but these sampling methods can be too qualititative for many ecological analyses and too destructive for monitoring purposes. Advances in the use of underwater videography and still imagery for biodiversity habitat mapping and modelling have been used within Geoscience Australia to extract data related to sponge biodiversity patterns across three regions. In the new Oceanic Shoals Commonwealth Marine Reserve, sponge morphologies were characterized from still images to locate areas in which biodiversity may be high due to habitat-forming taxa. In the Carnarvon Shelf abundance of a target sponge (Cinachyrella sp.) was quantified from video to investigate relationships between biology and sediment characteristics. Around Lord Howe Island, benthic habitats are being analysed to the national standard of classification using both video and still images. Importantly specialists within ecology, geophysics and spatial statistics work together to integrate biological and physical data to provide unique and meaningful maps of predicted distributions and habitat suitability for key ecological benthic habitats.

Geoscience Australia carried out marine surveys in Jervis Bay (NSW) in 2007, 2008 and 2009 (GA303, GA305, GA309, GA312) to map seabed bathymetry and characterise benthic environments through colocated sampling of surface sediments (for textural and biogeochemical analysis) and infauna, observation of benthic habitats using underwater towed video and stills photography, and measurement of ocean tides and wavegenerated currents. Data and samples were acquired using the Defence Science and Technology Organisation (DSTO) Research Vessel Kimbla. Bathymetric mapping, sampling and tide/wave measurement were concentrated in a 3x5 km survey grid (named Darling Road Grid, DRG) within the southern part of the Jervis Bay, incorporating the bay entrance. Additional sampling and stills photography plus bathymetric mapping along transits was undertaken at representative habitat types outside the DRG. jb_s4 is an ArcINFO grid of southern part of Jervis Bay survey area (south4 is part of Darling RD grid) produced from the processed EM3002 bathymetry data using the CARIS HIPS and SIPS software

A defining characteristic of the seabed is the proportion that is hard, or immobile. For marine ecosystems, hard seabed provides the solid substrate needed to support sessile benthic communities, often forming 'hotspots' of biodiversity such as coral and sponge gardens. For the offshore resource and energy industry, knowledge of the distribution of hard versus soft seabed is important for planning infrastructure (pipelines, wells) and to managing risk posed by geo-hazards such as migrating sand waves or mass movements on steep banks. Maps that delineate areas of hard and soft seabed are therefore a key product to the informed management and use of Australia's vast marine estate. As part of the Australian Government's Offshore Energy Security Program (2007-2011) and continuing under the National CO2 Infrastructure Plan (2011-2015), Geoscience Australia has been developing integrated seabed mapping methods to better map and predict seabed hardness using acoustic data (multibeam sonar), integrated with information from biological and physical samples. The first method used was a two-stage, classification-based clustering method. This method uses acoustic backscatter angular response curves to derive a substrate type map. The angular response curve is the backscatter value as a function of the incidence angle, where this angle lies between the incident acoustic signal from the normal. The second method was a prediction-based classification, using a machine learning method called random forest. This method was based on bathymetry, backscatter data and their derivatives, as well as underwater video and sediment data. The techniques developed by Geoscience Australia offer a fast and inexpensive assessment of the seabed that can be used where intensive seabed sampling is not feasible. Moreover, these techniques can be applied to areas where only multibeam acoustic data are available. Importantly, the identification of seabed substrate types in spatially continuous maps provides valuable baseline information for effective marine conservation management and infrastructure development.

The Leeuwin Current has significant ecological impact on the coastal and marine ecosystem of south-western Australia. This study investigated the spatial and temporal dynamics of the Leeuwin Current using monthly MODIS SST dataset between July 2002 and December 2012. Topographic Position Index layers were derived from the SST data for the mapping of the spatial structure of the Leeuwin Current. The semi-automatic classification process involves segmentation, 'seeds' growing and manual editing. The mapping results enabled us to quantitatively examine the current's spatial and temporal dynamics in structure, strength, cross-shelf movement and chlorophyll a characteristic. It was found that the Leeuwin Current exhibits complex spatial structure, with a number of meanders, offshoots and eddies developed from the current core along its flowing path. The Leeuwin Current has a clear seasonal cycle. During austral winter, the current locates closer to the coast (near shelf break), becomes stronger in strength and has higher chlorophyll a concentrations. While, during austral summer, the current moves offshore, reduces its strength and chlorophyll a concentrations. The Leeuwin Current also has notable inter-annual variation due to ENSO events. In El Niño years the current is likely to reduce strength, move further inshore and increase its chlorophyll a concentrations. The opposite occurs during the La Niña years. In addition, this study also demonstrated that the Leeuwin Current has a significantly positive influence over the regional nutrient characteristics during the winter and autumn seasons.