The cartographic collection of the Doc Fisher Geoscience Library consists of the maps and air photos created or acquired by agency staff since the formation of BMR in 1946. This includes maps produced by agencies which have merged with these over the years, such as AUSLIG. Maps held include: Australian geological map series (1:250,000, 1:100,000 and the 1 mile series); topographic maps produced by NATMAP and its predecessors (1:250,000, 1:100,000 and 1:50,000) - latest editions only; various Australian geochemical, geophysical and other thematic maps; geoscience map series from other countries acquired on an exchange basis, including some with accompanying explanatory notes; Non-series maps acquired by donation or exchange; atlases. The Air photos are predominantly those used for mapping Australia and, to a lesser extent, Papua New Guinea and Antarctica, by BMR/AGSO from the 1940s to the 1980s. Geographical coverage of the sets is not complete, but many individual photos are unique in that they have pin points, overlays or other markings made by teams in the field. The Papua New Guinea photographs in the collection may, in many cases, be the only existing copies. Flight diagrams are also held for many (but not all) sets of air photos. Some other related materials, such as montages of aerial photographs (orthophotos), are also represented in the collection.

This dataset maps the geomorphic habitat environments (facies) for 213 Queensland coastal waterways. This version of the dataset includes 73 newly mapped estuaries, classified as 'Near pristine'. The classification system contains 12 easily identifiable and representative environments: Barrier/back-barrier, Bedrock, Central Basin, Channel, Coral, Flood- and Ebb-tide Delta, Fluvial (bay-head) Delta, Intertidal Flats, Mangrove, Rocky Reef, Saltmarsh/Saltflat, Tidal Sand Banks (and Unassigned). These types represent habitats found across all coastal systems in Australia. Southern and central Great Barrier Reef lagoon coasts have a broad spectrum of river, tide and wave- dominated estuaries.

The 1:2.5M scale geology of Australia data documents the distribution and age of major stratigraphic, intrusive and medium to high-grade metamorphic rock units of onshore Australia. This edition contains the same geological content as the previous edition, but is structured according to Geoscience Australia's 2010 data standards and is provided in additional digital formats. The dataset was compiled to use at scales between 1:2.5 million and 1:5 million inclusive. The units distinguished/mapped mainly represent stratigraphic supergroups, regional intrusive associations and regional metamorphic complexes. Groupings of Precambrian units in the time-space diagram are generally separated by major time breaks; Phanerozoic units are grouped according to stratigraphic age i.e. System/Period. The time-space diagram has the added benefit that it provides a summary of units currently included on the themes. The method used to distinguish sedimentary and many volcanic units varies for each geological eon as follows: <ul><li>Cainozoic units are morphological units which emphasise the relationship of the sedimentary fill to the landscape.</li> <li>Mesozoic units are regionally extensive to continent-wide time-rock units which emphasise the System of Period(s).</li> <li>Palaeozoic units are stratotectonic units that emphasise either the dominant System or Period(s) or the range of Periods.</li> <li>Proterozoic units are commonly regional stratotectonic units - separated by major time breaks and split into the Palaeoproterozoic, Mesoproterozoic and Neoproterozoic Eras - which are generally unique to each cratonic region.</li> <li>Archaean units are regional lithological units grouped into broad time divisions.</li> <li>Metamorphic units are lithological units which emphasise the metamorphic facies and timing of the last major metamorphic event. </li> <li>Igneous units are regional units which emphasise the dominant lithology and are grouped into broad time divisions.</li></ul>

Displays the coverage of publicly available digital gamma-ray spectrometric data. The map legend is coloured according to the line spacing of the survey with broader line spacings (lower resolution surveys) displayed in shades of blue. Closer line spacings (higher resolution surveys are displayed in red, purple and coral.

Monitoring changes in the spatial distribution and health of biotic habitats requires spatially extensive surveys repeated through time. Although a number of habitat distribution mapping methods have been successful in clear, shallow-water coastal environments (e.g. aerial photography and Landsat imagery) and deeper (e.g. multibeam and sidescan sonar) marine environments, these methods fail in highly turbid and shallow environments such as many estuarine ecosystems. To map, model and predict key biotic habitats (seagrasses, green and red macroalgae, polychaete mounds [Ficopamatus enigmaticus] and mussel clumps [Mytilus edulis]) across a range of open and closed estuarine systems on the south-west coast of Western Australia, we integrated post-processed underwater video data with interpolated physical and spatial variables using Random Forest models. Predictive models and associated standard deviation maps were developed from fine-scale habitat cover data. Models performed well for spatial predictions of benthic habitats, with 79-90% of variation explained by depth, latitude, longitude and water quality parameters. The results of this study refine existing baseline maps of estuarine habitats and highlight the importance of biophysical processes driving plant and invertebrate species distribution within estuarine ecosystems. This study also shows that machine-learning techniques, now commonly used in terrestrial systems, also have important applications in coastal marine ecosystems. When applied to video data, these techniques provide a valuable approach to mapping and managing ecosystems that are too turbid for optical methods or too shallow for acoustic methods.

Data gathered in the field during the sample collection phase of the National Geochemical Survey of Australia (NGSA) has been used to compile the Preliminary Soil pH map of Australia. The map, which was completed in late 2009, offers a first-order estimate of where acid or alkaline soil conditions are likely to be expected. It provides fundamental datasets that can be used for mineral exploration and resource potential evaluation, environmental monitoring, landuse policy development, and geomedical studies into the health of humans, animals and plants.

Continent-scale digital maps of mineral information of the Earth's land surface are now achievable using geoscience-tuned remote sensing systems. Multispectral ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer) data and the derived mineral information provide the opportunity for characterization of geological and soil processes including the nature of the regolith (weathered) cover and alteration footprints of hydrothermal ore deposits [1,2]. This paper describes work from the Western Australian (WA) Centre of Excellence for 3D Mineral Mapping, which is part of CSIRO's Minerals Down Under Flagship and supported by Geoscience Australia and other Australian geosurveys, to generate a series of ASTER mineral group maps (both content and composition) for the whole Australian continent at a 30 m pixel resolution.. The input ASTER L1B radiance-at-sensor data were provided by ERSDAC (Japan), NASA and the USGS. These data were corrected for instrument, illumination, atmospheric and geometric effects. About 4000 ASTER scenes from an archive of >30,000 scenes were selected to generate the continent-scale ASTER map and Hyperion scenes were used for reduction and validation of the cross-calibrated ASTER mosaic to reflectance. Band ratios [2] were applied as base algorithms and masked to remove complicating effects, such as green vegetation, clouds and deep shadow. Types of generated geoscience products include (1) mineral group content maps based on continuum-band depths (e.g. Al-OH group content mapping Al-OH clays like muscovite, kaolinite and montmorillonite) and (2) mineral group composition maps (e.g. Al-OH group composition ranging from Si-rich white mica through to well ordered kaolinite) based on ratios but masked using the relevant content products.

This study tested the performance of 16 species models in predicting the distribution of sponges on the Australian continental shelf using a common set of environmental variables. The models included traditional regression and more recently developed machine learning models. The results demonstrate that the spatial distributions of sponge as a species group can be successfully predicted. A new method of deriving pseudo-absence data (weighted pseudo-absence) was compared with random pseudo-absence data - the new data were able to improve modelling performance for all the models both in terms of statistics (~10%) and in the predicted spatial distributions. Overall, machine learning models achieved the best prediction performance. The direct variable of bottom water temperature and the resource variables that describe bottom water nutrient status were found to be useful surrogates for sponge distribution at the broad regional scale. This study demonstrates that predictive modelling techniques can enhance our understanding of processes that influence spatial patterns of benthic marine biodiversity. Ecological Informatics

2nd edition Available as a GA Library resource.

Bathurst NSW regolith-landforms map 1:250 000