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>Antarctic Specially Protected Area (ASPA) No. 143 Marine Plain in East Antarctica is valued for its “outstanding fossil fauna and rare geological features” (ATCM XXXVI 2013) but scientific evidence to guide its protection is sparse. The fragile Sørsdal Formation contains diverse marine vertebrate and invertebrate fauna, preserving a unique record of Antarctic environmental conditions in the Pliocene (Quilty et al. 2000). Strict permitting and access conditions are in place for the ASPA but evidence-based guidance for decision makers on how to assess the risks to the values of the ASPA is minimal. </div><div><br></div><div>We will present the results of geological mapping, aerial imagery collection, and field observations from Marine Plain to consider the impact of foot traffic and helicopter access to the ASPA and provide options for future monitoring and management. Surficial geology of the broader Vestfold Hills (a 400 km2 ice-free region in East Antarctica) was mapped at 1:2000 scale using aerial photos, satellite imagery, a digital elevation model, and field observations (McLennan et al. 2021, McLennan et al. 2022). From this regional-scale mapping, we show that the glacial sediments draping bedrock hills in Marine Plain are typical of the Vestfold Hills region and do not represent the vulnerable Sørsdal Formation or the thermokarst features considered unique to the ASPA. Polygons outlining recommended landing areas for helicopters in the Marine Plain ASPA were derived using a buffer around the Sørsdal Formation, lakes, away from the edge of steep bedrock slopes, and higher that the limit of Pliocene marine inundation. Our results show how foundational datasets like landform and geomorphology mapping can provide robust evidence to support informed ASPA management. </div><div><br></div><div>ATCM XXXIV, 2013. Measure 9. Antarctic Specially Protected Area No 143 (Marine Plain, Mule Peninsula, Vestfold Hills, Princess Elizabeth Land): Revised Management Plan</div><div>McLennan S. M., Haiblen A. M. & Smith J. 2021 Surficial geology of the Vestfold Hills, East Antarctica. First ed. Canberra, Australia: Geoscience Australia. https://pid.geoscience.gov.au/dataset/ga/145535</div><div>McLennan S. M., Haiblen, A.M. & Smith, J. 2022 Surficial geology of the Vestfold Hills, East Antarctica, GIS dataset. Canberra, Australia: Geoscience Australia. https://pid.geoscience.gov.au/dataset/ga/145536</div><div>Quilty P. G., Lirio J. M. & Jillett D. 2000 Stratigraphy of the Pliocene Sørsdal Formation, Marine Plain, Vestfold Hills, East Antarctica, <em>Antarctic Science</em>, vol. 12, no. 2, pp. 205-216. Presented at the SCAR Open Science Conference 2024

<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>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>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>

<div>This data package contains interpretations of airborne electromagnetic (AEM) conductivity sections in the Exploring for the Future (EFTF) program’s Eastern Resources Corridor (ERC) study area, in south eastern Australia. Conductivity sections from 3 AEM surveys were interpreted to provide a continuous interpretation across the study area – the EFTF AusAEM ERC (Ley-Cooper, 2021), the Frome Embayment TEMPEST (Costelloe et al., 2012) and the MinEx CRC Mundi (Brodie, 2021) AEM surveys. Selected lines from the Frome Embayment TEMPEST and MinEx CRC Mundi surveys were chosen for interpretation to align with the 20&nbsp;km line-spaced EFTF AusAEM ERC survey (Figure 1).</div><div>The aim of this study was to interpret the AEM conductivity sections to develop a regional understanding of the near-surface stratigraphy and structural architecture. To ensure that the interpretations took into account the local geological features, the AEM conductivity sections were integrated and interpreted with other geological and geophysical datasets, such as boreholes, potential fields, surface and basement geology maps, and seismic interpretations. This approach provides a near-surface fundamental regional geological framework to support more detailed investigations. </div><div>This study interpreted between the ground surface and 500&nbsp;m depth along almost 30,000 line kilometres of nominally 20&nbsp;km line-spaced AEM conductivity sections, across an area of approximately 550,000&nbsp;km2. These interpretations delineate the geo-electrical features that correspond to major chronostratigraphic boundaries, and capture detailed stratigraphic information associated with these boundaries. These interpretations produced approximately 170,000 depth estimate points or approximately 9,100 3D line segments, each attributed with high-quality geometric, stratigraphic, and ancillary data. The depth estimate points are formatted for compliance with Geoscience Australia’s (GA) Estimates of Geological and Geophysical Surfaces (EGGS) database, the national repository for standardised depth estimate points. </div><div>Results from these interpretations provided support to stratigraphic drillhole targeting, as part of the Delamerian Margins NSW National Drilling Initiative campaign, a collaboration between GA’s EFTF program, the MinEx CRC National Drilling Initiative and the Geological Survey of New South Wales. The interpretations have applications in a wide range of disciplines, such as mineral, energy and groundwater resource exploration, environmental management, subsurface mapping, tectonic evolution studies, and cover thickness, prospectivity, and economic modelling. It is anticipated that these interpretations will benefit government, industry and academia with interest in the geology of the ERC region.</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>Maps of seabed geomorphology derived from bathymetry data provide foundational information that is used to support the sustainable use of the marine environment across a range of activities that contribute to the Blue Economy. The global recognition of the value of the Blue Economy and several key global initiatives, notably the Seabed 2030 project to map the global ocean and the United Nations Decade of Ocean Science for Sustainable Development, are driving the proliferation and open dissemination of these data and derived map products. To effectively support these global efforts, geomorphic characterisation of the seabed requires standardized multi-scalar and interjurisdictional approaches that can be applied locally, regionally and internationally. This document describes and illustrates a geomorphic lexicon for the full range of coastal to deep ocean geomorphic Settings and related Processes that drive the formation, modification and preservation of geomorphic units on the seabed. Terms and Settings/Processes have been selected from the literature and structured to balance established terminology with the need for consistency between the range of geomorphic Settings. This document also presents a glossary of the terms and identifies the insights that can be gained by mapping each unit type, from an applied perspective.</div> <b>Citation:</b> Nanson, Rachel, Arosio, Riccardo, Gafeira, Joana, McNeil, Mardi, Dove, Dayton, Bjarnadóttir, Lilja, Dolan, Margaret, Guinan, Janine, Post, Alix, Webb, John, & Nichol, Scott. (2023). <i>A two-part seabed geomorphology classification scheme; Part 2: Geomorphology classification framework and glossary (Version 1.0) (1.0).</i> Zenodo. https://doi.org/10.5281/zenodo.7804019

<div><em>Seabed geomorphology</em> describes the shape and evolution of underwater landscapes. These landscapes interact with ocean currents to create diverse marine habitats. Similar to geological maps on land, maps of seabed geomorphology are vital for making informed decisions to support the sustainable growth of our Ocean Economy.</div><div><br></div><div>As we gather more detailed seabed data and face increasing ocean pressures, there's a need for new, standardised maps that support consistent decision making at multiple scales and between administrative jurisdictions. Dr Rachel Nanson and an international team have developed a new seabed geomorphology classification system that is designed to simplify complex seabed interpretations into a map format that is accessible to a broad range of end users.</div><div><br></div><div>This approach is being adopted internationally and is currently being implemented by Geoscience Australia. We are using the method to map parts of Australia’s extensive Marine Park network and to support government to make informed decisions regarding Australia’s rapidly expanding Offshore Renewable Energy sector</div>

<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>