Removing the topographic effect from satellite images is a very important step in order to obtain comparable surface reflectance in mountainous areas and to use the images for different purposes on the same spectral base. The most common method of normalising for the topographic effect is by using a Digital Surface Model (DSM) and / or a Digital Elevation Model (DEM). However, the accuracy of the correction depends on the accuracy, scale and spatial resolution of DSM data as well as the co-registration between the DSM and satellite images. A physics based BRDF and atmospheric correction model in conjunction with a 1-second SRTM (Shuttle Radar Topographic Mission) derived DSM product released by Geoscience Australia in 2010 were used to conduct the analysis reported in this paper. The results show that artefacts in the DSM data can cause significant local errors in the correction. For some areas, false shadow and over corrected surface reflectance factors have been observed. In other areas, the algorithm is unable to detect shadow or retrieve an accurate surface reflectance factor in the slopes away from the sun. The accuracy of co-registration between satellite images and DSM data is crucial for effective topographic correction. A mis-registration error of one or two pixels can lead to large error of retrieved surface reflectance factors in the gully and ridge areas (retrieved reflectance factors can change from 0.3 to 0.5 or more). Therefore, accurate registrations for both satellite images and DSM data are necessary to ensure the accuracy of the correction. Using low resolution DSM data in conjunction with high resolution satellite images can fail to correct some significant terrain effects. A DSM resolution appropriate to the scale of the resolution of satellite image is needed for the best results.

The developed method of long-strip adjustment for orientation and georeferencing of PRISM imagery is based on the merging of successive images within a single satellite pass into what amounts to a single image covering the entire orbit segment. Metadata for each separate scene is merged to produce a single, continuous set of orbit and attitude parameters, such that the entire strip of tens of images can be treated as a single image, even though the separate scenes are not actually merged. Within the strip adjustment, the orbit parameters are refined based on the provision of GCPs at each end of the strip. A minimum of four GCPs is required to achieve 1-pixel georeferencing accuracy, even for strip lengths of 1000 km or more. The merging of orbit data results in a very considerable reduction in both the number of unknown orientation parameters and the number of required GCPs in the sensor orientation adjustment. Indeed the number of required GCPs can drop from well over 100 to only 4-6 for a 50-image orbit segment. Moreover, unlike in traditional photogrammetric strip adjustment, there is no need for tie-point measurements between images. Once the adjusted orbit parameters are obtained, the georeferencing and orthorectification process can revert to a fully automatic image-by-image computation. Following orthorectification, a final mosaicking is undertaken to produce the reference image, namely the AGRI. AGRI was needed because imagery from emerging new satellites can be automatically registered to it, consistently and accurately. AGRI was made possible by the developed long-strip adjustment approach to satellite image georeferencing. This technique, implemented in Barista, rendered the project feasible in time, logistics and cost. It reduced the image registration problem from correction of almost 10,000 scenes to correction of just 105 orbit segments. Moreover, the number of required GCPs was reduced from more than 30,000 to less than 1000.

Extensive benefits and tools can be gained for mineral explorers, land-users and government and university researchers using new spectral data and processing techniques. Improved methods were produced as part of a large multi-agency project focusing on the world-class Mt Isa mineral province in Australia. New approaches for ASTER calibration using high-resolution HyMap imagery through to testing for compensation for atmospheric residuals, lichen and other vegetation cover effects have been included in this study. . Specialised data processing software capable of calibrating and processing terabytes of multi-scene imagery and a new approach to delivery of products, were developed to improve non-specialist user interpretation and comparison with other datasets within a GIS. Developments in processing and detailed reporting of methodology, accuracies and applications can make spectral data a more functional and valuable tool for users of remote sensing data. A highly-calibrated approach to data processing, using PIMA ground samples to validate the HyMap, and then calibrating the ASTER data with the HyMap, allows products to have more detailed reliable accuracies and integration with other data, such as geophysical and regolith information in a GIS package, means new assessments and interpretations can be made in mapping and characterising materials at the surface. Previously undiscovered or masked surface expression of underlying materials, such as ore-deposits, can also be identified using these methods. Maps and products made for this project, covering some ~150 ASTER scenes and over 200 HyMap flight-lines, provide a ready-to-use tool that aids explorers in identifying and mapping unconsolidated regolith material and underlying bedrock and alteration mineralogy.

Large areas of prospective North and North-East Queensland have been surveyed by airborne hyperspectral sensor, HyMap, and airborne geophysics as part of the 'Smart' exploration initiative by the Geological Survey of Queensland. In particular, 25000 km2 of hyperspectral mineral and compositional map products, at 4.5 m spatial resolution, have been generated and made available via the internet. In addition, more than 130 ASTER scenes were processed and merged to produce broad scale mapping of mineral groups (Thomas et al, 2008). Province-scale, accurate maps of mineral abundances and minerals chemistries were generated for North Queensland as a result of a 2 year project starting in July 2006 which involved CSIRO Exploration and Mining, the Geological Survey of Queensland (GSQ), Geoscience Australia, James Cook University, and Curtin University. Airborne radiometric data acquired over the same North Queensland Mt Isa - Cloncurry areas as the hyperspectral surveys, had been acquired at flight line spacing of 200 metre. Such geophysical radiometric data provides a useful opportunity to compare the mineral mapping potential of both techniques, for a wide range of geological and vegetated environments. In this study, examples are described of soil mapping within the Tick Hill area, and geological / exploration mapping within the Mt Henry and Suicide Ridge prospects of North Queensland.

New ASTER GIS products in the Gawler-Curnamona Geoscience Australia, in collaboration with CSIRO and PIRSA are releasing a suite of 14 new ASTER mosaiced products for a significant part of the Gawler-Curnamona region. About 110 ASTER scenes have been mosaiced and processed into geoscience products that can be quickly and easily integrated with other datasets in a GIS. The products have been pre-processed and calibrated with available HyMap data and provide basic mineral group information such as Ferric Oxide abundance, AlOH group distribution as well as mosaiced and levelled false colour and regolith ratio images. These images, along with accompany notes are available for free ftp download online at: ftp://ftp.arrc.csiro.au/NGMM/Gawler-Curnamona ASTER Project/

Legacy product - no abstract available

Legacy product - no abstract available

Preliminary regolith mapping of the Highland Rocks region using Landsat MSS and high resolution gamma-ray spectrometric imagery: Australian Geological Survey Organisation. 18 pages; 6 fig, 12 ref.

Legacy product - no abstract available

Londonderry - Drysdale TMI (rtp) with northeast illumination