This web service contains marine geospatial data held by Geoscience Australia. It includes bathymetry and backscatter gridded data plus derived layers, bathymetry coverage information, bathmetry collection priority and planning areas, marine sediment data and other derived products. It also contains the 150 m and optimal resolution bathymetry, 5 m sidescan sonar (SSS) and synthetic aperture sonar (SAS) data collected during phase 1 and 2 marine surveys conducted by the Governments of Australia, Malaysia and the People's Republic of China for the search of Malaysian Airlines Flight MH370 in the Indian Ocean. This web service allows exploration of the seafloor topography through the compilation of multibeam sonar and other marine datasets acquired.

This web service contains marine geospatial data held by Geoscience Australia. It includes bathymetry and backscatter gridded data plus derived layers, bathymetry coverage information, bathmetry collection priority and planning areas, marine sediment data and other derived products. It also contains the 150 m and optimal resolution bathymetry, 5 m sidescan sonar (SSS) and synthetic aperture sonar (SAS) data collected during phase 1 and 2 marine surveys conducted by the Governments of Australia, Malaysia and the People's Republic of China for the search of Malaysian Airlines Flight MH370 in the Indian Ocean. This web service allows exploration of the seafloor topography through the compilation of multibeam sonar and other marine datasets acquired.

This web service contains marine geospatial data held by Geoscience Australia. It includes bathymetry and backscatter gridded data plus derived layers, bathymetry coverage information, bathmetry collection priority and planning areas, marine sediment data and other derived products. It also contains the 150 m and optimal resolution bathymetry, 5 m sidescan sonar (SSS) and synthetic aperture sonar (SAS) data collected during phase 1 and 2 marine surveys conducted by the Governments of Australia, Malaysia and the People's Republic of China for the search of Malaysian Airlines Flight MH370 in the Indian Ocean. This web service allows exploration of the seafloor topography through the compilation of multibeam sonar and other marine datasets acquired.

A biophysical dispersal model was used to simulate hydrodynamic connectivity among canyons located within Australia's South-west marine region. The results show that exchange among canyons in this area is greatly influenced by the Leeuwin current, transporting larvae in a unidirectional manner around Cape Leeuwin, and continuing eastwards along the Great Australian Bight. Larvae within canyons tend to remain within them, however if they are transported above the canyon walls, they then have the opportunity to be transported significant distances (thousands of kilometres). Analysis of the variability in connectivity patterns reveals concentrated flow near the shelf break, with increasing levels of variability leading offshore from the canyons. While the average potential flow distance and duration between canyons were approximately 550 kilometres and 33 days respectively, the average realized flow distance and duration were approximately 30 kilometres and 6 days respectively. This study provides the first consideration of connectivity among submarine canyons and will help improve management of these features by providing a better understanding of larval movement, transboundary exchange and the potential spread of invasive species.

The aim of this study was to apply a seascape modelling approach to explain patterns of genetic patchiness across a complex coral reef system. We focus our analysis on the tabulate coral species Acropora spicifera, which is considered rare throughout the Indo-Pacific and East Indian Ocean, but dominates coral assemblages of the HAI at the southern extent of its range (Veron and Marsh 1988). We coupled population genetic data from a panel of microsatellites DNA markers with a biophysical dispersal model to test whether oceanographic processes could predict patterns of local genetic structure. Our results demonstrate that incorporating an estimate of resistance to connectivity imposed by regional oceanographic currents can predict patterns of genetic divergence and enhance our understanding of processes driving connectivity across complex seascapes.

The dominant ocean current off the Western Australian (WA) coast is the Leeuwin Current (LC) [1]. It is a warm, poleward flowing surface current up to 300 metres in depth and exhibits significant seasonal differences in intensity, ranging from strong during the austral winter and weak during the austral summer [2-3]. At a regional scale, the LC is significant because it directly influences the temperature and nutrient dynamics of the WA ocean ecosystem [3]. As a result, it has been shown that the LC affects the production of phytoplankton [4-5], the recruitment of western rock lobster [6], and the distribution of fishes and algae [7]. The LC can be observed from Sea Surface Temperature (SST) satellite-derived images. However, delineating an accurate map showing the extent and spatial structure of the LC from a SST image remains a challenge. And given the large area covered by the LC, an automatic approach is desirable. This study aims to test an object-based image processing technique from time-series MODIS SST data for the above purpose. SeaDas image processing software was used to process MODIS images from daily raw data to Level 3 products. The monthly SST4 layers between June 2009 and May 2010 were the inputs for this study. The SST layer shown in Figure 1a clearly indicates a warmer (than off-shore) southward flowing current (LC) that extends from Exmouth, passing Cape Leeuwin, into the Great Australian Bight. Previously, relative temperature differences have been employed to identify LC structure from SST images [8]. An off-shore SST profile crossing the LC (Figures 1a & b) shows that the LC, indicated by warmer temperatures, occupies a zone approximately between 10 km and 90 km from the coast, with a core current between 35 km and 70 km. This study utilised two characteristics of MODIS-derived sea surface temperatures to identify the extents of the LC. The first characteristic is that the LC is warmer than surrounding waters. The second characteristic is the connectivity between the core LC current and the eddies. According to the first characteristic, the SST images were treated as elevation surfaces where the LC occupies slope and ridge positions. Topographic Position Index (TPI) was then derived from these SST layers to identify topographic positions [9]. As shown in Figure 1c, the LC approximately corresponds with areas of large positive TPI values. In the next step, the multi-resolution algorithm in eCognition Developer was employed to segment the SST and TPI layers of each month into objects. The objects were classified into a pseudo LC class if their mean TPI values are greater than 0.25 of the global standard deviation value. The second characteristic of the LC was then used to remove false positive objects. To do that, a small number of objects at known LC locations were selected as 'seeds'. In a looping process, any objects that connect with these seeds were classified as true LC class. The extents of the LC for the 12 months analysed here areshown in Figure 2. The LC during austral winter is clearly stronger (e.g., larger in extent) than during austral summer, which confirms the findings of other studies [2-3]. The LC during the summer time is patchier, which required more seeds (8-14) than during the winter time (less than 5 seeds). The core summer current is also slightly further away from the coast. In addition, eddies are clearly visible in most months. In summary, the proposed object-based approach was semi-automatic and effective in delineating the extents of the LC although there is a degree of subjectivity in the selection of accurate seeds. The weak summer current, however poses some difficulty for the approach and future work is aimed at improving the modelling accuracy.