<div>This data product contains geospatial seabed morphology and geomorphology information for the Beagle Marine Park and is intended for use by marine park managers, regulators, the general public and other stakeholders. A nationally consistent two-part (two-step) seabed geomorphology classification system was used to map and classify the distribution of key seabed features. </div><div><br></div><div>In step 1, semi-automated GIS mapping tools (GA-SaMMT; Huang et al., 2022; eCat Record 146832) were applied to bathymetry digital elevation models (DEM) in a GIS environment (ESRI ArcGIS Pro) to map polygon extents (topographic high, low, and planar) and quantitatively characterise their geometries. The geometric attributes were then used to classify each shape into discrete Morphology Feature types (Part 1: Dove et al., 2020; eCat Record 144305). In step 2, the seabed geomorphology was interpreted by applying additional datasets and domain knowledge to inform their geomorphic characterisation (Part 2: Nanson et al., 2023; eCat Record 147818). Where available, backscatter intensity, seabed imagery, seabed sediment samples and sub-bottom profiles supplemented the bathymetry DEM and morphology classifications to inform the geomorphic interpretations.</div><div><br></div><div>The Beagle Marine Park seabed morphology and geomorphology features were informed by a post survey report (Barrett et al., 2021). Seabed units were classified at multiple resolutions that were informed by the underlying bathymetry: </div><div><br></div><div>· A broad scale layer represents features that were derived from a 30 m horizontal resolution compilation DEM (Beaman et al 2022; eCat Record 147043). </div><div>· A series of medium and fine scale feature layers were derived from individual 1 m horizontal resolution DEMs (Nichol et al., 2019; eCat Record 130301). </div><div><br></div><div>The data product and application schema are fully described in the accompanying Data Product Specification. </div><div><br></div><div><em>Barrett, N, Monk, J., Nichol, S., Falster, G., Carroll, A., Siwabessy, J., Deane, A., Nanson, R., Picard, K., Dando, N., Hulls, J., and Evans, H. (2021). Beagle Marine Park Post Survey Report: South-east Marine Parks Network. Report to the National Environmental Science Program, Marine Biodiversity Hub. University of Tasmania.</em></div><div><br></div><div><em>Beaman, R.J. (2022). High-resolution depth model for the Bass Strait -30 m. <a href=https://dx.doi.org/10.26186/147043>https://dx.doi.org/10.26186/147043</a>, GA eCat Record 147043.&nbsp;</em></div><div><br></div><div><em>Dove, D., Nanson, R., Bjarnadóttir, L. R., Guinan, J., Gafeira, J., Post, A., Dolan, Margaret F.J., Stewart, H., Arosio, R., Scott, G. (2020). A two-part seabed geomorphology classification scheme (v.2); Part 1: morphology features glossary. Zenodo. <a href=https://doi.org/10.5281/zenodo.40752483>https://doi.org/10.5281/zenodo.4075248</a>; GA eCat Record 144305&nbsp;</em></div><div><br></div><div><em>Huang, Z., Nanson, R. and Nichol, S. (2022). Geoscience Australia's Semi-automated Morphological Mapping Tools (GA-SaMMT) for Seabed Characterisation. Geoscience Australia, Canberra. <a href=https://dx.doi.org/10.26186/146832>https://dx.doi.org/10.26186/146832</a>; GA eCat Record 146832 </em></div><div><em>&nbsp;</em></div><div><em>Nanson, R., Arosio, R., Gafeira, J., McNeil, M., Dove, D., Bjarnadóttir, L., Dolan, M., Guinan, J., Post, A., Webb, J., Nichol, S. (2023). A two-part seabed geomorphology classification scheme; Part 2: Geomorphology classification framework and glossary (Version 1.0) (1.0). Zenodo.<a href=https://doi.org/10.5281/zenodo.7804019>https://doi.org/10.5281/zenodo.7804019</a>; GA eCat Record 147818&nbsp;</em></div>

<div>The geological data includes the spatial extents of the Kati Thanda - Lake Eyre Basin (KT-LEB) project area, geological basin and sub-basin boundaries, and geological models of the extent and thickness of the main Cenozoic sedimentary packages in the KT-LEB in central Australia. This data package has particular focus on the geological Lake Eyre Basin (LEB) and its main sedimentary depocentres of the Callabonna and Tirari sub-basins, and the Cooper Creek Palaeovalley. The new geological datasets available in this data package were developed as part of the project on the Cenozoic geology, hydrogeology, and groundwater systems of the Kati Thanda - Lake Eyre Basin, the results of which were published in Evans et al. (2024). This activity was undertaken as part of the National Groundwater Systems project in the Geoscience Australia Exploring for the Future program.</div><div><br></div><div>This geological data package contains the following eight datasets:</div><div>1. Spatial extents of the boundary of the KT–LEB project area.</div><div>2. Major sites of Cenozoic sediment deposition within the KT-LEB.</div><div>3. Total thickness of Cenozoic sediments in KT-LEB, with derived contours, hillshaded image and Cenozoic cover extent. </div><div>4. Saturated thickness model of Cenozoic sediments in the KT-LEB with derived contours, hillshaded image and Cenozoic cover extent.</div><div>5. Model of the base of Cenozoic surface of the KT-LEB project area, with derived contours, hill-shaded image and Cenozoic cover extent.</div><div>6. Model of thickness of Quaternary sediments of the KT-LEB with derived contours, hillshaded image and the Quaternary sediments extent outline.</div><div>7. Model of thickness of Namba Formation in KT-LEB, with derived contours, hillshaded image and the Namba Formation extent outline.</div><div>8. Model of thickness of Eyre Formation in KT-LEB with derived contours, hillshaded image and the Eyre Formation extent outline.</div><div><br></div><div>Reference:</div><div>Evans TJ, Bishop C, Symington NJ, Halas L, Hansen JWH, Norton CJ, Hannaford C and Lewis SJ (2024) <em>Cenozoic geology, hydrogeology, and groundwater systems: Kati Thanda – Lake Eyre Basin</em>, Record 2024/05, Geoscience Australia, Canberra, http://dx.doi.org/10.26186/147422.</div><div><br></div>

This data product contains seabed morphology and geomorphology information for a subset area of Zeehan Marine Park. A nationally consistent seabed geomorphology classification scheme was used to map and classify the distribution of key seabed features. The Zeehan Marine Park seabed morphology and geomorphology maps were derived from a 2 m horizontal resolution bathymetry DEM compiled from a multibeam survey undertaken for Parks Australia by the University of Tasmania. Semi-automated GIS mapping tools (GA-SaMMT); (Huang et. al., 2022; eCat Record 146832) were applied to a bathymetry digital elevation model (DEM) in a GIS environment (ESRI ArcGIS Pro) to map polygon extents (topographic high, low, and planar) and to quantitatively characterise polygon geometries. Geometric attributes were then used to classify each shape into discrete Morphology Feature types (Dove et. al., 2020; eCat Record 144305). Seabed geomorphology features were interpreted by applying additional datasets and domain knowledge to inform their geomorphic characterisation (Nanson et. al., 2023; eCat Record 147818). Where available, backscatter intensity, seabed imagery, and survey reports supplemented the bathymetry DEM and morphology classifications to inform the geomorphic interpretations. The data product and classification schema are fully described in the accompanying Data Product Specification. Dove, D., Nanson, R., Bjarnadóttir, L. R., Guinan, J., Gafeira, J., Post, A., Dolan, Margaret F.J., Stewart, H., Arosio, R., Scott, G. (2020). A two-part seabed geomorphology classification scheme (v.2); Part 1: morphology features glossary. Zenodo. https://doi.org/10.5281/zenodo.4075248; Huang, Z., Nanson, R., Nichol, S. 2022. Geoscience Australia's Semi-automated Morphological Mapping Tools (GA-SaMMT) for Seabed Characterisation. Geoscience Australia, Canberra. https://dx.doi.org/10.26186/146832 Nanson, R., Arosio, R., Gafeira, J., McNeil, M., Dove, D., Bjarnadóttir, L., Dolan, M., Guinan, J., Post, A., Webb, J., Nichol, S. (2023). A two-part seabed geomorphology classification scheme; Part 2: Geomorphology classification framework and glossary (Version 1.0) (1.0). Zenodo. https://doi.org/10.5281/zenodo.7804019

<div>This data product contains geospatial seabed morphology and geomorphology information for Flinders Reefs and Cairns Seamount (Coral Sea Marine Park). These maps are intended for use by marine park managers, regulators, the general public and other stakeholders. A nationally consistent two-part (two-step) seabed geomorphology classification system was used to map and classify the distribution of key seabed features. </div><div><br></div><div>In step 1, semi-automated GIS mapping tools (GA-SaMMT; Huang et al., 2022; eCat Record 146832) were applied to a bathymetry digital elevation model (DEM) in a GIS environment (ESRI ArcGIS Pro) to map polygon extents (topographic high, low, and planar) and to quantitatively characterise their geometries. Their geometric attributes were then used to classify each shape into discrete Morphology Feature types (Part 1: Dove et al., 2020; eCat Record 144305). In step 2, the seabed geomorphology was interpreted by applying additional datasets and domain knowledge to inform their geomorphic characterisation (Part 2: Nanson et al., 2023; eCat Record 147818). Where available, backscatter intensity, seabed imagery, seabed sediment samples and sub-bottom profiles supplemented the bathymetry DEM and morphology classifications to inform the geomorphic interpretations.</div><div><br></div><div>The Flinders Reefs seabed morphology and geomorphology maps were derived from an 8 m horizontal resolution bathymetry DEM compiled from multibeam surveys (FK200429/GA4861: Beaman et al., 2020; FK200802/GA0365: Brooke et al, 2020), Laser Airborne Depth Sounder (LADS), Light Detection and Ranging (LiDAR) and bathymetry supplied by the Australian Hydrographic Office.</div><div><br></div><div>A subset of the FK200802/GA0365 multibeam survey was gridded at 1 m horizontal resolution to derive the key morphology and geomorphology features at the top of Cairns Seamount (-35 to -66 m; within the upper mesophotic zone).</div><div><br></div><div>The data product and application schema are fully described in the accompanying Data Product Specification. </div><div><br></div><div><em>Beaman, R., Duncan, P., Smith, D., Rais, K., Siwabessy, P.J.W., Spinoccia, M. 2020. Visioning the Coral Sea Marine Park bathymetry survey (FK200429/GA4861). Geoscience Australia, Canberra. <a href=https://dx.doi.org/10.26186/140048>https://dx.doi.org/10.26186/140048</a>; GA eCat record 140048</em></div><div><br></div><div><em>Brooke, B., Nichol, S., Beaman, R. 2020. Seamounts, Canyons and Reefs of the Coral Sea bathymetry survey (FK200802/GA0365). Geoscience Australia, Canberra. <a href=https://dx.doi.org/10.26186/144385>https://dx.doi.org/10.26186/144385</a>; GA eCat record 144385</em></div><div><br></div><div><em>Dove, D., Nanson, R., Bjarnadóttir, L. R., Guinan, J., Gafeira, J., Post, A., Dolan, Margaret F.J., Stewart, H., Arosio, R., Scott, G. (2020). A two-part seabed geomorphology classification scheme (v.2); Part 1: morphology features glossary. Zenodo. <a href=https://doi.org/10.5281/zenodo.4075248>https://doi.org/10.5281/zenodo.4075248</a>; GA eCat Record 144305 </em></div><div><br></div><div><em>Huang, Z., Nanson, R. and Nichol, S. (2022). Geoscience Australia's Semi-automated Morphological Mapping Tools (GA-SaMMT) for Seabed Characterisation. Geoscience Australia, Canberra. <a href=https://dx.doi.org/10.26186/146832>https://dx.doi.org/10.26186/146832</a>; GA eCat Record 146832</em></div><div><br></div><div><em>Nanson, R., Arosio, R., Gafeira, J., McNeil, M., Dove, D., Bjarnadóttir, L., Dolan, M., Guinan, J., Post, A., Webb, J., Nichol, S. (2023). A two-part seabed geomorphology classification scheme; Part 2: Geomorphology classification framework and glossary (Version 1.0) (1.0). Zenodo. <a href=https://doi.org/10.5281/zenodo.7804019>https://doi.org/10.5281/zenodo.7804019</a>; GA eCat Record 147818 </em></div>

Accurate information about the extent, frequency and duration of forest inundation is required to inform ecological, biophysical and hydrological models and enables floodplain managers to quantify the efficacy of flood mitigation/modification activities. Open water classifiers derived from optical remote sensing typically underestimate or fail to detect floodplain forest inundation. This paper presents a new method for detecting forest inundation dynamics using freely available Landsat and Sentinel 2 data, referred to as short-wave infrared mapping under vegetation. The method uses a dynamic threshold that accounts for the additional shortwave infrared reflectance caused by the presence of tree canopies over floodwater. The method is demonstrated at five Ramsar listed River Red Gum floodplain forest wetlands in southeastern Australia. Accuracy assessment based on independent drone imagery from a wide range of vegetated wetlands showed an absolute accuracy of 67%–70% and a fuzzy accuracy of 81%–83%. We found the method is conservative, and underestimates inundation (16%–18%) but very rarely misclassifies dry pixels as inundated (0.3%–0.6%). When compared to river gauge data, the method shows similar trends to an open water classifier (i.e., the area of inundated vegetation increases with increasing river height). The method is conservative compared to lidar-based floodplain inundation models but can be applied wherever cloud-free scenes of Landsat or Sentinel 2 have been acquired, thereby enabling floodplain managers with the ability to quantify changes in inundation dynamics in places/time-periods where lidar is unavailable. <b>Citation:</b> Lymburner, L., Ticehurst, C., Adame, M. F., Sengupta, A., & Kavehei, E. (2024). Seeing the floods through the trees: Using adaptive shortwave infrared thresholds to map inundation under wooded wetlands. <i>Hydrological Processes</i>, 38(6), e15174. https://doi.org/10.1002/hyp.15174

<div>The Geological and Bioregional Assessment Program collected an extensive LiDAR elevation dataset focused on Cooper Creek Floodplain in Queensland and South Australia. The LiDAR data was collected by Fugro Australia Ltd in two aerial surveys in 2019 covering a total survey area of 31,780 km2 across the Cooper Creek Floodplain, and the Thomson and Barcoo river systems (GBA 2021). The data was acquired at an average density of 1 point per square metre, processed and compiled as LiDAR Classified Data in LAS 1 km tiles and 1 m grid DEM in ESRI ascii 1 km tiles. As part of the study of the <em>Cenozoic geology, hydrogeology and groundwater systems of Kati Thanda - Lake Eyre Basin</em> for the National Groundwater Systems project (Exploring for the Future program) (see Evans et al. 2024) these 1 km tiles were mosaiced into a seamless grid and resampled to 10 m cell resolution raster images for ease of visualisation and usability across GIS applications (refer to lineage field of this metadata record for the complete reference details of publications cited in this abstract).</div>

<div>Australia has a vast marine jurisdiction and a thriving ocean economy, but our ocean faces increasingly complex pressures. Advancing our ocean knowledge is key to unlocking a sustainable ocean future. Seabed maps are an essential source of baseline information to inform the conservation, restoration, sustainable use, and management of our oceans.</div><div><br></div><div>The goal is to map the shape of Australia’s seabed in sufficient detail to inform the sustainable management and use of marine resources. But knowing how much of the seabed is “mapped” and what is “sufficient” are far from simple. Seabed (bathymetry) data is available from multiple sources, is collected using different techniques of variable quality, is stored in disparate formats and locations, and what is considered sufficient varies depending on the application. As a result, Australia’s progress in mapping its seabed cannot be determined simply from data coverage.</div><div><br></div><div>This fact sheet defines the term “mapped in sufficient detail”, provides an up-to-date assessment of the proportion of seabed mapped in Australia, and lays the foundation for reporting future progress.</div><div><br></div><div>As of August 2024, using this methodology, 35.2% of Australia’s marine jurisdiction has been mapped in sufficient detail, with more of the seabed mapped around mainland Australia and external territories than in the Australian Antarctic Territory’s Exclusive Economic Zone (EEZ).</div><div><br></div><div>Geoscience Australia will continue working with the seabed mapping community to include new and legacy data in the coverage dataset and will continue to track and report on Australia’s progress in mapping the seabed across its marine jurisdiction.</div>

<div>The development of Australia’s offshore renewable energy (ORE) industry can learn and benefit from decades of international experience and research. However, local knowledge of our unique receiving environment and the organisms that depend on it is critical for ensuring development minimises impacts on marine ecosystems. Long-term monitoring and adaptive management strategies that consistently evaluate and address environmental impacts of offshore wind farms will be necessary throughout the operational lifespan of ORE. This collaborative National Environmental Science Program project established an inventory of environmental and cultural data and best practice monitoring standards to support regulatory decision-making for ORE development for current proposed and declared areas: Hunter, Gippsland and Bass Strait, Illawarra, Southern Ocean and south-west Western Australia. We provide detail on 1) potential impacts of installation, operation, and decommissioning; 2) best practice standards for monitoring; 3) cultural and environmental values of Indigenous communities with links to development areas; 4) seabed geomorphology and habitat characterisation; potential interactions with oceanography and 5) the seasonality and distribution of interacting species. The inventory, which is available to the Government, proponents, and researchers, will improve the effectiveness of future research for the sustainable development of ORE in Australia. Presented at the 2024 AMSA-NZMSS Conference Hobart Tas

<div><strong>Output Type: </strong>Exploring for the Future Extended Abstract</div><div><br></div><div><strong>Short Abstract: </strong>Most geological mapping either over-estimates the amount of bedrock exposed at the surface or can miss local bedrock exposures in geological units describing cover materials (i.e. alluvium and colluvium). A machine learning, Convolutional Neural Network (CNN) has been applied to detect outcrops (exposed bedrock at the earth’s surface) and areas of very shallow cover over bedrock (i.e. sub-crop) at one meter resolution. We used a multi-feature training dataset consisting of sites associated with urban areas, roads, outcrops, waterbodies, soil (includes bare soil and soil covered by green and dry vegetation), trees, and shadows. Even though we were only interested in mapping outcrop, a multi-criteria label set significantly improved overall accuracy of the model. The explanatory variables or covariates included high-resolution satellite imagery, Sentinel-2 imagery, and terrain derivatives. The modelling approach was tested over an area in central West NSW, Australia. Labels were split into 80% for training and 20% for out-of-sample validation. Spatial K-groups were used in the training set to minimize auto-spatial correlation between neighbouring points and reduce the potential for overfitting. Two CNN model architectures were evaluated: Simple-Net and UNet. The Simple-Net structure consists of 2D Convolution layer and flatten layer, whereas the UNet architecture includes a mixture of 2D convolution layer, max pooling, up sampling and flatten layer. These models were tested with and without the use of high-resolution imagery. The UNet model incorporating high resolution imagery gave the best results (accuracy of 0.841 and an F1 score of 0.814), compared with Simple-Net (accuracy of 0.823 and an F1 score of 0.786). However, the Simple-Net’s model without the incorporation of high-resolution imagery was a slight improvement over the UNet architecture and due to the lack of national coverage for high-resolution imagery, the Simple-Net model offers better scalability. The detection of outcrop/sub-crop has broad application in improving the spatial explicitness of existing geological maps, improving sample detection and interpretation of litho-stratigraphy and geochemistry. High spatial resolution of outcrop/sub-crop also has implications in the agricultural and civil engineering sectors, ecology and in understanding surface and near-surface hydrological systems. After this proof-of-concept phase we plan to up-scale the approach nationally using a more representative labels and national covariates.</div><div><br></div><div><strong>Citation: </strong>Du, Z., Wilford, J. &amp; Roberts, D., 2024. Find a rock or a rock nearby using Convolution Neural Networks. In: Czarnota, K. (ed.) Exploring for the Future: Extended Abstracts. Geoscience Australia, Canberra. https://doi.org/10.26186/149631</div>

<div>Australia’s vast marine estate offers high-quality offshore wind resources that have the potential to help produce the renewable energy that Australia will need to achieve its net zero emissions targets. Mature offshore renewable industries in Europe have demonstrated that marine geoscience is critical for supporting the sustainable development, installation, operation and decommissioning of offshore wind farms. Geoscience information is used to design targeted seabed surveys and identify areas suitable for offshore infrastructure, thereby reducing uncertainty and investment risk. These data also provide important regional context for environmental impact assessments and informs evidence-based decisions consistent with government policies and regulations. Effective geomorphic characterisation of the seabed requires a standardised, multi-scalar and collaborative approach to produce definitive geomorphology maps that can support these applications. These maps synthesise interpretations of bathymetry, shallow geology, sedimentology and ecology data, to illustrate the distribution and diversity of seabed features, compositions and processes, including sediment dynamics and seabed stability. We present mapped examples demonstrating the utility of a nationally consistent seabed geomorphology mapping scheme (developed in collaboration with European agencies), for application to a broad range of geographic settings and policy-needs, including the sustainable development of offshore renewable energy in Australia. Presented at the 2024 AMSA-NZMSS Conference Hobart Tas