2nd edition Available as a GA Library resource.

Geoscience Australia has created a DVD 'Landsat Metadata Map Ups of Indonesia' for the Indonesian Ministry of Forestry (MoF). The DVD contains Landsat metadata information sourced from USGS and GISTDA for selected years based on the catalogue searches that Geoscience Australia has done to-date. This is one of the action items from the Bali Remote Sensing workshop in February 2009.

Gravity station location map, updated to October 2007

No abstract available

The use of airborne hyperspectral imagery for mapping soil surface mineralogy is examined for the semi-arid Tick Hill test site (20 km2) near Mount Isa in north-western Queensland. Mineral maps at 4.5 m pixel resolution include the abundances and physicochemistries (chemical composition and crystal disorder) of kaolin, illite-muscovite, and Al smectite (both montmorillonite and beidellite), as well as iron oxide, hydrated silica (opal), and soil/rock water (bound and unbound). Validation of these hyperspectral mineral maps involved field sampling (34 sites) and laboratory analyses (spectral reflectance and X-ray diffraction). The field spectral data were processed for their mineral information content the same way as the airborne HyMap data processing. The results showed significant spatial and statistical correlation. The mineral maps provide more detailed surface compositional information compared with the published soil and geology maps and other geoscience data (airborne radiometrics and digital elevation model). However, there is no apparent correlation between the published soil types (i.e. Ferrosols, Vertosols, and Tenosols) and the hyperspectral mineral maps (e.g. iron oxide-rich areas are not mapped as Ferrosols and smectite-rich areas are not mapped as Vertosols). This lack of correlation is interpreted to be related to the current lack of spatially comprehensive mineralogy for existing regional soil mapping. If correct, then this new, quantitative mineral mapping data has the potential to improve not just soil mapping but also soil and water catchment monitoring and modeling at local to regional scales. The challenges to achieving this outcome include gaining access to continental-scale hyperspectral data and models that link the surface mineralogy to subsurface soil characteristics/processes.

Remotely sensed imagery has been used extensively in geomorphology since the availability of early Landsat data. Since that time, there has been a steady increase in the range of sensors offering data with increased spatial and spectral resolutions, from both government and commercial satellites. This has been augmented with an increase in the amount and range of airborne surveys carried out. Since 2000, digital elevation models have become widely available through the application of interferometric synthetic aperture radar, photogrammetry and laser altimetry (specifically LiDAR) with extensive uptake by geomorphologists. In addition, hyperspectral imaging, radiometrics and electromagentics have been made more accessible, whilst there has been increased use of close-range (<200 m) imaging techniques for very high resolution imaging. This paper reviews the primary sources for DEMs from satellite and airborne platforms, as well as briefly reviewing more traditional multi-spectral scanners, and radiometric and electromagnetic systems. Examples of the applications of these techniques are summarised and presented within the context of landscape pattern recognition and modelling. Finally, the wider issues of access to geographic information and data distribution are discussed.

Iron (Fe) oxide mineralogy in most Australian soils is poorly characterised, even though Fe oxides play an important role in soil function. Fe oxides reflect the conditions of pH, redox potential (Eh), moisture and temperature in the soil environment. The Fe oxide mineralogy exerts a strong control on soil colour. Visible-near infrared (vis-NIR) spectroscopy can be used to identify and measure the abundance of certain Fe oxides in soil as well as soil colour. The aims of this paper are to: (i) measure the hematite and goethite content of Australian soils from their vis-NIR spectra, (ii) compare these results to measurements of soil colour, and (iii) describe the spatial variability of hematite, goethite and soil colour, and map their distribution across Australia. The spectra of 4606 surface soil sample from across Australia were measured using a vis-NIR spectrometer with a wavelength range between 350-2500 nm. We determined the Fe oxide content from characteristic absorptions of hematite (near 880 nm) and goethite (near 920 nm) and derived a normalised iron oxide difference index (NIODI) to better discriminate between them. The NIODI was generalised across Australia with its spatial uncertainty using sequential indicator simulation. We also derived soil RGB colour from the spectra and mapped its distribution and uncertainty across the country using sequential Gaussian simulations. The simulated RGB colour values were made into a composite true colour image and were also converted to Munsell hue, value and chroma. These colour maps were compared to the map of the NIODI and both were used for interpretation of our results. The work presented here was evaluated using existing studies on the distribution of Fe oxides in Australian soils.

A new digital surface geology dataset covering Australia at 1:1 million scale was released recently by Geoscience Australia. The digital map, which depicts geological units and structures seamlessly across state and territory borders, will provide an invaluable baseline dataset for national and regional evaluation of resources as well as environmental management and land use decision-making. This national project was undertaken with the full co-operation of the geological surveys of each Australian state and the Northern Territory who provided their most recent map data for the national compilation as well as their advice in resolving stratigraphic issues.

Map(s) of Si (silicon) concentration (Total content, Aqua Regia soluble content, and/or Mobile Metal Ion soluble content) in Top Outlet Sediment (TOS) and/or Bottom Outlet Sediment (BOS) samples, dry-sieved to <2 mm and/or <75 um grain size fractions. Source: The Geochemical Atlas of Australia (Caritat and Cooper, 2011)

Map(s) of Ta (tantalum) concentration (Total content, Aqua Regia soluble content, and/or Mobile Metal Ion soluble content) in Top Outlet Sediment (TOS) and/or Bottom Outlet Sediment (BOS) samples, dry-sieved to <2 mm and/or <75 um grain size fractions. Source: The Geochemical Atlas of Australia (Caritat and Cooper, 2011)