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  • The Dynamic Land Cover Dataset Version 2 is a suite of land cover information products from Geoscience Australia (GA). These information products deliver International Standards Organisation (ISO) compliant land cover maps across the Australian landmass. The datasets provide a consistent series of maps that show how Australian land cover is changing over time. The current version consists of 14 maps each based on 2 years of MODIS data. The 14 maps cover the period from January 2001 - December 2015. The Dynamic Land Cover Dataset uses a standard land cover classification to show the change in behaviour of land cover across Australia. The DLCD includes data for every 250m by 250m area on the ground, for the period 2001 to 2015. The DLDC provides a basis for reporting on change and trends in vegetation cover and extent. Information about land cover dynamics is essential to understanding and addressing a range of national challenges such as drought, salinity, water availability and ecosystem health. The current release of the second version DLCDv2.1 presents land cover information for every 250m by 250m area of the country for each of the two year intervals listed in the table below. It consists of maps based on 2 years of MODIS EVI time-series data. The date ranges for each of the map series are: • January 2001-December 2002 • January 2002-December 2003 • January 2003-December 2004 • January 2004-December 2005 • January 2005-December 2006 • January 2006-December 2007 • January 2007-December 2008 • January 2008-December 2009 • January 2009-December 2010 • January 2010-December 2011 • January 2011-December 2012 • January 2012-December 2013 • January 2013-December 2014 • January 2014-December 2015 DLCD can be used as an input for a wide range of environmental modelling applications, including: • Climate • Wind and water erosion risk • Evapotranspiration • Carbon dynamics • Land surface processes In conjunction with other data sources, the DLCD can be used to identify emerging patterns of land cover change and provide a spatial and historical context within which to interpret change. The land cover classification scheme used conforms to the 2007 International Standards Organisation (ISO) land cover standard (19144-2). The dataset shows Australian land covers clustered into 22 classes. These reflect the structural character of vegetation, ranging from cultivated and managed land covers (crops and pastures) to natural land covers such as trees and grasslands.

  • This dataset details the Declared Indigenous Protected Areas (IPA) across Australia through the implementation of the Indigenous Protected Areas Programme. These boundaries are not legally binding. An Indigenous Protected Area (IPA) is an area of Indigenous-owned land or sea where traditional Indigenous owners have entered into an agreement with the Australian Government to promote biodiversity and cultural resource conservation. The Indigenous Protected Areas element of the Caring for our Country initiative supports Indigenous communities to manage their land as IPAs, contributing to the National Reserve System. Further information can be found at the website below. http://www.environment.gov.au/indigenous/ipa/index.html Declared IPAs in order of gazettal date: Nantawarrina Preminghana Risdon Cove putalina Deen Maar Yalata Warul Kawa Watarru Walalkara Mount Chappell Island Badger Island Dhimurru Guanaba Wattleridge Mount Willoughby Paruku Ngaanyatjarra Tyrendarra Toogimbie Anindilyakwa Laynhapuy - Stage 1 Ninghan North Tanami Warlu Jilajaa Jumu Kaanju Ngaachi Great Dog Island Babel Island lungatalanana Angas Downs Pulu Islet Tarriwa Kurrukun Warddeken Djelk Jamba Dhandan Duringala Kurtonitj Framlingham Forest Kalka - Pipalyatjara Boorabee and The Willows Lake Condah Marri-Jabin (Thamurrurr - Stage 1) Brewarrina Ngemba Billabong Uunguu - Stage 1 Apara - Makiri - Punti Antara - Sandy Bore Dorodong Weilmoringle Yanyuwa (Barni - Wardimantha Awara) Minyumai Gumma Mandingalbay Yidinji Southern Tanami Angkum - Stage 1 Ngunya Jargoon Birriliburu Eastern Kuku Yalanji Bardi Jawi Girringun Wilinggin Dambimangari Balanggarra Thuwathu/Bujimulla Yappala Wardaman - Stage 1 Karajarri - Stage 1 Nijinda Durlga - Stage 1 Warraberalgahl and Porumalgal Kiwirrkurra Nyangumarta Warrarn Matuwa Kurrara-Kurrara

  • Terrain illumination correction is an important step in the normalisation of remotely sensed data for the inversion of land surface parameters, and for applications that aim to detect land surface change through time series analysis. To accurately normalise for the terrain effect, an appropriate resolution of the Digital Elevation Model (DEM) data with sufficient quality is critical for effective correction of remotely sensed data over mountainous areas. Conversely, using terrain illumination correction and scale-based analysis, such as filter bank analysis, the quality of DEM data can be evaluated. In this study, TanDEM-X Intermediate DEM (IDEM) data at 12 m and 30 m resolutions, and the 1-second SRTM data (~ 30 m resolution) were used to evaluate their effectiveness for terrain illumination correction of Landsat satellite data. The island of Tasmania in Australia has a fine scale of terrain detail as well as high relief. The high latitude and strong variability in the terrain illumination throughout the year make it an ideal study site for applying the methods available for this evaluation. Results from the terrain illumination correction and filter bank analysis show that IDEM 12 m and 30 m resolution datasets can resolve finer details of terrain shading than the SRTM based DEMs and deliver better results in the areas with detail-rich terrain. However, since the data available for this study is an intermediate product, spikes and other noise artefacts were prevalent, especially over areas covered by water. Operational use of the IDEM would require the removal of such noise artefacts. The filter bank analysis also found that both Landsat panchromatic data and IDEM 12 m data are oversampled and the signal-to-noise parameters for both DEM and Landsat data are yet to be fully established. Further evaluation of the relative merits of the TanDEM-X based DEM data and the SRTM based DEM data for terrain illumination correction would be possible when the WorldDEM product based on TanDEM-X data becomes routinely available.

  • Eonomic Fairways Explorer video presentation for PDAC 2015. The purpose of this video demonstration is to show the Proof of Concept (PoC) of the Economic Fairways Explorer application, which enables users to perform "what if" economic modelling and scenarios using GIS data. The Economic Fairways Explorer application is based upon the CIAP framework.

  • Moreton Island and several other large siliceous sand dune islands and mainland barrier deposits in SE Queensland represent the distal, onshore component of an extensive Quaternary continental shelf sediment system. This sediment has been transported up to 1000 km along the coast and shelf of SE Australia over multiple glacioeustatic sea-level cycles. Stratigraphic relationships and a preliminary Optically Stimulated Luminance (OSL) chronology for Moreton Island indicate a middle Pleistocene age for the large majority of the deposit. Dune units exposed in the centre of the island and on the east coast have OSL ages that indicate deposition occurred between approximately 540 ka and 350 ka BP, and at around 96 ± 10 ka BP. Much of the southern half of the island has a veneer of much younger sediment, with OSL ages of 0.90 ± 0.11 ka, 1.28 ± 0.16 ka, 5.75 ±0.53 ka and <0.45 ka BP. The younger deposits were partially derived from the reworking of the upper leached zone of the much older dunes. A large parabolic dune at the northern end of the island, OSL age of 9.90 ± 1.0 ka BP, and palaeosol exposures that extend below present sea level suggest the Pleistocene dunes were sourced from shorelines positioned several to tens of metres lower than, and up to few kilometres seaward of the present shoreline. Given the lower gradient of the inner shelf a few km seaward of the island, it seems likely that periods of intermediate sea level (e.g. ~20 m below present) produced strongly positive onshore sediment budgets and the mobilisation of dunes inland to form much of what now comprises Moreton Island. The new OSL ages and comprehensive OSL chronology for the Cooloola deposit, 100 km north of Moreton Island, indicate that the bulk of the coastal dune deposits in SE Queensland were emplaced between approximately 540 ka BP and prior to the Last Interglacial. This chronostratigraphic information improves our fundamental understanding of long-term sediment transport and accumulation on large-scale continental shelf sediment systems.

  • This dataset contains sediment and geochemistry information for the Oceanic Shoals Commonwealth Marine Reserve (CMR) in the Timor Sea collected by Geoscience Australia during September and October 2012 on RV Solander (survey GA0339/SOL5650). Further information on the survey is available in the post-survey report published as Geoscience Australia Record 2013/38: Nichol, S.L., Howard, F.J.F., Kool, J., Stowar, M., Bouchet, P., Radke, L., Siwabessy, J., Przeslawski, R., Picard, K., Alvarez de Glasby, B., Colquhoun, J., Letessier, T. & Heyward, A. 2013. Oceanic Shoals Commonwealth Marine Reserve (Timor Sea) Biodiversity Survey: GA0339/SOL5650 - Post Survey Report. Record 2013/38. Geoscience Australia: Canberra. (GEOCAT #76658).

  • Historical offshore wave buoy records are invaluable for understanding past and future wave climates as well as the associated erosion risk to coastlines. Yet, the usefulness of buoy records can be limited by short record lengths, time gaps, sparse spatial coverage, and until recently, many wave buoys did not have the capability to measure wave directions. To help address this issue, we present an inverse wave ray tracing approach to transfer wave data from intermediate depths to deep water. This method could be extended to minimize site specific wave direction bias when combining multiple buoy records to synthesize continuous time series. The ray tracing approach applied, first proposed by [1], is given in Equation 0.1. This simple approach of transferring wave directions from intermediate to deep depths using inverse ray tracing is illustrated for the case at the Crowdy Head waverider buoy (New South Wales, Australia), which, is located in approximately 80 metres depth and is offshore a coastal erosion hotspot. Without the effects of refraction, waves travel in great circles on the spherical surface of the earth as illustrated in Figure 0.1 A). A typical rule of thumb is that refraction begins to effect waves when the water depth is half the wavelength. Inverse wave ray traces for 14s waves are shown in Figure 0.1 B). Refraction was calculated for 8,10,12,14 and 16 second waves at 5 degree azimuth increments with the results condensed into Figure 0.1 C). This analysis agrees with expectations that refraction has little effect on waves with incident angles normal to the coast, while refraction is substantial for oblique incident angles and long period waves. To illustrate, this method suggests that 14s waves arriving at the Crowdy Head waverider buoy from 180 degrees, were refracted by 15 degrees (i.e. 195 degree angle at 100 km prior to their arrival). Essentially, this method quantifies changes in wave directions caused by refraction over complex bathymetry between deep and intermediate water depths. Lookup tables can be developed for multiple sites to allow wave directions at one location to be transferred to deep water and then back to another nearby buoy location to fill potential gaps in the record. This paper supports the Bushfire and Natural Hazards Cooperative Research Centre project Resilience to clustered disaster events on the coast storm surge , that aims to quantify the impact and risk of coincident and clustered disasters on the coast, with an initial focus on storm surge, associated erosion and reshaping of the coastline and the resulting inundation and damage to buildings and infrastructure[2].

  • Understanding the distribution of subsurface temperatures is an important early step in a geothermal exploration process. A new approach for developing a 3D temperature map of the Australian continent is being developed by combining available proxy data using high-performance computing and large continental-scale datasets. Underworld is used to estimate the steady-state thermal profile of the Australian continent, and the uncertainties of this estimate, by modelling a suite of different scenarios. The geophysical properties of each scenario are determined using national-scale datasets including geological mapping and geochemical samples, amongst others. Though only in the early stages of development, the modelling has successfully demonstrated that there are greater amounts of geoscientific data available in Australia by which to estimate broad-scale subsurface temperatures than have previously been utilised.

  • Analysis of the distribution patterns of Pb isotope data from mineralised samples using the plumbotectonic model of Carr et al. (1995), which invokes mixing between crustal and mantle reservoirs, indicates systematic spatial patterns that reflect major metallogenic and tectonic boundaries in the Paleozoic Lachlan and Delamerian orogens in New South Wales and Victoria, Australia. This distribution pattern accurately maps the boundary between the Central and Eastern Lachlan subprovinces. The Central Lachlan Subprovince is characterised by Pb isotope characteristics with a strong crustal signature, whereas the Eastern Lachlan Subprovince is characterised by variable crustal and mantle signatures. The Macquarie Volcanic Province is dominated by Pb with a mantle signature: known porphyry Cu-Au and high sulphidation epithermal Au-Cu deposits in the province are associated with a zone characterised by the strongest mantle signatures. In contrast, granite-related Sn deposits in the Central Lachlan Subprovince are characterised by the strongest crustal signatures. The Pb isotope patterns are broadly similar to Nd isotope model age patterns derived from felsic magmatic rocks, although a lower density of Nd isotope data locations makes direct comparison difficult. The two reservoirs identified by Carr et al. (1995) do not appear to be isotopically linked: the crustal source was not formed via extraction from the mantle source. Rather, the two reservoirs formed separately. The mantle reservoir may have been sourced from a subducting proto-Pacific plate, whereas the crustal reservoir is most likely to be extended Australian crust. The data allow the possibility that the proto-Pacific mantle source was isotopically linked to the western Tasmanian crustal source. Comparison of Pb isotope data from the Girilambone district, Central Lachlan Subprovince, (e.g., Tritton and Avoca Tank Cu deposits) with those from the Cobar Cu-Au-Zn-Pb district, Eastern lachlan Subprovince, in north central New South Wales indicates a less radiogenic signature, and probably older age, for deposits in the Girilambone district. Hence, a syngenetic volcanic-associated massive sulphide origin for these deposits is preferred over a syn-tectonic origin. The data are also consistent with formation of the Girilambone deposits in a back-arc basin inboard from the earliest phase of the Macquarie Volcanic Province.

  • Reducing uncertainty at an early stage of resource development is a key necessity to attract project finance. Risk analysis frameworks exist in the petroleum industry for quantifying risk and expected returns (Newendorp, 1975; Suslick et al., 2009). For deep Enhanced Geothermal Systems (EGS) and Hot Sedimentary Aquifers (HSA), there is limited knowledge and experience available from in-the-ground projects to make informed estimates of the likelihood of outcomes for incorporation into a risk analysis framework. Modelling incorporating uncertainty analysis based on a library of EGS and HSA geothermal reservoirs, together with proxy data, could be used to develop a Geothermal Play Systems framework for assessing reservoir risk and ranking prospects.