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.

Terrain affects optical satellite images through both irradiance and BRDF effects. It results in the slopes facing toward the sun receiving enhanced solar irradiance and appearing brighter compared to those facing away from the sun. For anisotropic surfaces, the radiance received at the satellite sensor from a sloping surface is also affected by surface BRDF which varies with combinations of surface landcover types, sun, and satellite as well as topographic geometry. Consequently, to obtain comparable surface reflectance from satellite images covering mountainous areas, it is necessary to process the images to reduce or remove the topographic effect so that the images can be used for different purposes. The most common method of normalising for the topographic effect is by using a Digital Surface Model (DSM). 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 physically based BRDF and atmospheric correction model in conjunction with the 1-second SRTM derived DSM product were used to conduct the analysis. 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. The accuracy of co-registration between satellite images and DSM data is important for the topographic correction. A mis-registration error of one or two pixels can lead to large error in the gully and ridge areas. Therefore, accurate registration for both satellite images and DSM data is necessary to ensure the accuracy of the correction. Using low resolution DSM data to correct high resolution satellite images can fail to correct some significant terrain effects.

Pixel Quality Assessment describes the results of a number of quality tests which are used to determine the quality of a Landsat image product in terms of, pixel saturation, pixel contiguity between spectral bands, whether the pixel is over land or sea, cloud contamination, cloud shadow and topographic shadow. Pixel Quality is used to filter an input Landsat image for downstream processing in a production workflow. It has general applicability to a number of image processing scenarios.

Spectral data from airborne and ground surveys enable mapping of the mineralogy and chemistry of soils in a semi-arid terrain of Northwest Queensland. The study site is a region of low relief, 20 km southeast of Duchess near Mount Isa. The airborne hyperspectral survey identified more than twenty surface components including vegetation, ferric oxide, ferrous iron, MgOH, and white mica. Field samples were analysed by spectrometer and X-ray diffraction to test surface units defined from the airborne data. The derived surface materials map is relevant to soil mapping and mineral exploration, and also provides insights into regolith development, sediment sources, and transport pathways, all key elements of landscape evolution.

Londonderry - Drysdale TMI (rtp) with northeast illumination

Identifying and mapping regolith materials at the regional and continental-scale can be facilitated via a new generation of remote sensing methods and standardised geoscience products. The multispectral Advanced Spaceborne Thermal Emission and Reflectance Radiometer (ASTER) is the first Earth observation (EO) system to acquire complete coverage of the Australian continent. The Japanese ASTER instrument is housed onboard the USA's Terra satellite, and has 14 spectral bands spanning the visible and near-infrared (VNIR - 500-1,000 nm - 3 bands @ 15 m pixel resolution); shortwave-infrared (SWIR - 1,000-2,500 nm range - 6 bands @ 30 m pixel resolution); and thermal infrared (TIR 8,000-12,000 nm - 90 m pixel resolution) with a 60 km swath. Although ASTER spectral bands do not have sufficient spectral resolution to accurately map the often small diagnostic absorption features of specific mineral species, which can be measured using more expensive 'hyperspectral' systems, current coverage of hyperspectral data is very restricted. The extensive coverage and 30m pixel size of ASTER make it well suited to national scale work. The spectral resolution of ASTER make it best suited to mapping broader 'mineral groups', such as the di-octahedral 'Al-OH' group comprising the mineral sub-groups (and their minerals species) like kaolins (e.g. kaolinite, dickite, halloysite), white micas (e.g. illite, muscovite, paragonite) and smectites (e.g. montmorillonite and beidellite). Extracting mineral group information using ASTER, using specially targeted band combinations, can find previously unmapped outcrop of bedrocks, weathering products, help define soil type and chemistry, and delineate and characterise regolith and landform boundaries over large and remote areas.

Legacy product - no abstract available

This bulk set comprises 10 sets of 5 image cards. The cards are the same as the single set of cards included in both the Discovering Remote Sensing kit and each student manual in the Discovering Remote Sensing bulk set (purchased separately). The image cards are used with the student activitities in each of the latter two Remote Sensing resources. Suitable for secondary years 8-12

This product includes the remote sensing information booklet + student activities + one set of five A4 image cards. Discovering Remote Sensing - an introduction does not contain any overhead projection images. Suitable for secondary Years 8-12.

No abstract available