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In many areas of the world, vegetation dynamics in semi-arid floodplain environments have been seriously impacted by increased river regulation and groundwater use. With increases in regulation along many rivers in the Murray-Darling Basin, flood volume, seasonality and frequency have changed which has in turn affected the condition and distribution of vegetation. Floodplain vegetation can be degraded from both too much and too little water due to regulation. Over-regulation and increased use of groundwater in these landscapes can exacerbate the effects related to natural climate variability. Prolonged flooding of woody plants has been found to induce a number of physiological disturbances such as early stomatal closure and inhibition of photosynthesis. However, drought conditions can also result in leaf biomass reduction and sapwood area decline. Depending on the species, different inundation and drought tolerances are observed. Identification of groundwater-dependent terrestrial vegetation, and assessment of the relative importance of different water sources to vegetation dynamics, typically requires detailed ecophysiological studies over a number of seasons or years as shown in Chowilla, New South Wales [] and Swan Coastal Plain, Western Australia []. However, even when groundwater dependence can be quantified, results are often difficult to upscale beyond the plot scale. Quicker, more regional approaches to mapping groundwater-dependent vegetation have consequently evolved with technological advancements in remote sensing techniques. Such an approach was used in this study. LiDAR canopy digital elevation model (CDEM) and foliage projected cover (FPC) data were combined with Landsat imagery in order to characterise the spatial and temporal behaviour of woody vegetation in the Lower Darling Floodplain, New South Wales. The multi-temporal dynamics of the woody vegetation were then compared to the estimated availability of different water sources in order to better understand water requirements.
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Legacy product - no abstract available
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Total magnetic intensity (TMI) data measures variations in the intensity of the Earth's magnetic field caused by the contrasting content of rock-forming minerals in the Earth crust. Magnetic anomalies can be either positive (field stronger than normal) or negative (field weaker) depending on the susceptibility of the rock. The data are processed via standard methods to ensure the response recorded is that due only to the rocks in the ground. The results produce datasets that can be interpreted to reveal the geological structure of the sub-surface. The processed data is checked for quality by GA geophysicists to ensure that the final data released by GA are fit-for-purpose. This Gawler Craton Airborne Survey Merge Magnetics - TMI RTP colourindex image is a pseudocolour image of the TMI grid of the Gawler Craton Airborne Survey Magnetic Radiometric and DEM Merge, SA, 2017-2019 survey. This grid used to produce this image has a cell size of 0.00042 degrees (approximately 40m). The data used to produce the TMI grid was acquired in 2017-2019 by the SA Government, and consisted of 1660000 line-kilometres of data at 200m line spacing and 60m terrain clearance. To constrain long wavelengths in the grid, an independent data set, the Australia-wide Airborne Geophysical Survey (AWAGS) airborne magnetic data, was used to control the base levels of the survey grid. This survey grid is essentially levelled to AWAGS. The data has had a variable reduction to the pole applied to centre the magnetic anomaly over the magnetised body. The VRTP processing followed a differential reduction to pole calculation up to 5th order polynomial. Magnetic inclination and declination were derived from the IGRF-11 geomagnetic reference model using a data representative date and elevation representative of the survey. This pseudocolour image shows the magnetic response of subsurface features with contrasting magnetic susceptibilities. The image can also be used to locate structural features such as dykes.
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The radiometric, or gamma-ray spectrometric method, measures the natural variations in the gamma-rays detected near the Earth's surface as the result of the natural radioactive decay of potassium (K), uranium (U) and thorium (Th). The data collected are processed via standard methods to ensure the response recorded is that due only to the rocks in the ground. The results produce datasets that can be interpreted to reveal the geological structure of the sub-surface. The processed data is checked for quality by GA geophysicists to ensure that the final data released by GA are fit-for-purpose. The Geological Survey of South Australia commissioned the Gawler Craton Airborne Survey (GCAS) as part of the PACE Copper initiative. The airborne geophysical survey was flown over parts of the Gawler Craton in South Australia. The program was designed to capture new baseline geoscientific data to provide further information on the geological context and setting of the area for mineral systems (http://energymining.sa.gov.au/minerals/geoscience/pace_copper/gawler_craton_airborne_survey). This radiometric uranium image has a cell size of 0.0004 degrees (approximately 41m) and shows uranium element concentration of the Gawler Craton Airborne Survey Magnetic Radiometric and DEM Merge, SA, 2017-2019 in units of parts per million (or ppm). Noise-adjusted singular value decomposition (NASVD) has been applied to the data. NASVD is a spectral component analysis procedure for the removal of noise from gamma-ray spectra. The data used to produce this image was acquired in 2019 by the SA Government, and consisted of 1660000 line-kilometres of data at 200m line spacing and 60m terrain clearance. To constrain long wavelengths in the grid, an independent data set, the Australia-wide Airborne Geophysical Survey (AWAGS) airborne magnetic data, was used to control the base levels of the survey grid.
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The radiometric, or gamma-ray spectrometric method, measures the natural variations in the gamma-rays detected near the Earth's surface as the result of the natural radioactive decay of potassium (K), uranium (U) and thorium (Th). The data collected are processed via standard methods to ensure the response recorded is that due only to the rocks in the ground. The results produce datasets that can be interpreted to reveal the geological structure of the sub-surface. The processed data is checked for quality by GA geophysicists to ensure that the final data released by GA are fit-for-purpose. The total dose rate is due to natural sources of radiation and is computed by adding estimates of cosmic dose at ground level to the terrestrial dose. The terrestrial dose rate grid is derived as a linear combination of the filtered K, U and Th grids. A low pass filter is applied to this grid to generate the filtered total dose rate grid. This Gawler Craton Airborne Survey Merge Radiometrics has a cell size of 0.0004 degrees (approximately 41m) and shows the total dose rate of the Gawler Craton Airborne Survey Magnetic Radiometric and DEM Merge, SA, 2017-2019 survey. The data used to produce this grid was acquired in 2017-2019 by the SA Government, and consisted of 1660000 line-kilometres of data at 200m line spacing and 60m terrain clearance.
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The Australian Geological Survey Organisation (AGSO) presents its solutions to mapping, GIS and image processing on the Internet. Software used is based on commercial and open source products. A distributed web mapping system is demonstrated, and concepts of distributed web mapping discussed. A model and prototype system for online delivery of satellite image data is presented. AGSO has been providing Internet access to spatial data since 1996. AGSO is the main repository for national geoscientific data, and services a wide range of clients across industry, government and the general public. Data provided range from point data, such as site descriptions and scientific analysis of samples, to line polygon and grid data, such as geological and geophysical surveys and associated maps. AGSO currently holds 500 MB of GIS data and a similar amount of image data on its web site, these data are expected to expand to a number of terabytes over the next few years. A primary role of AGSO is to provide its data to clients and stakeholders in as efficient a way as possible, hence its choice of Internet delivery. The major obstacle for supplying data of large volume over the Internet is bandwidth. Many AGSO clients are in remote locations with low bandwidth connections to the Internet. Possible solutions to this problem are presented. Examples of AGSO web tools are available at http://www.agso.gov.au/map/
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Total magnetic intensity (TMI) data measures variations in the intensity of the Earth's magnetic field caused by the contrasting content of rock-forming minerals in the Earth crust. Magnetic anomalies can be either positive (field stronger than normal) or negative (field weaker) depending on the susceptibility of the rock. The data are processed via standard methods to ensure the response recorded is that due only to the rocks in the ground. The results produce datasets that can be interpreted to reveal the geological structure of the sub-surface. The processed data is checked for quality by GA geophysicists to ensure that the final data released by GA are fit-for-purpose. . This Tanami NS 5ZB - TMI RTP 1VD grid (AWAGS) is the first vertical derivative of the TMI RTP grid of the NTGS Tanami NS 5ZB Region Detailed Airborne Magnetic Radiometric and Digital Elevation Survey, NT, 2018 survey. This grid has a cell size of 0.00018 degrees (approximately 20m) , and given in units of nT per metre (nT/m). The data used to produce the TMI grid was acquired in 2018 by the NT Government, and consisted of 3019 line-kilometres of data at 100m line spacing and 60m terrain clearance. The data has had a variable reduction to the pole applied to centre the magnetic anomaly over the magnetised body. The VRTP processing followed a differential reduction to pole calculation up to 5th order polynomial. Magnetic inclination and declination were derived from the IGRF-11 geomagnetic reference model using a data representative date and elevation representative of the survey. A first vertical derivative was calculated by applying a fast Fourier transform (FFT) process to the TMI RTP grid of the NTGS Tanami NS 5ZB Region Detailed Airborne Magnetic Radiometric and Digital Elevation Survey, NT, 2018 survey to produce this grid. This grid was calculated using an algorithm from the INTREPID Geophysics software package. This grid shows the magnetic response of subsurface features with contrasting magnetic susceptibilities. The grid can also be used to locate structural features such as dykes.
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Total magnetic intensity (TMI) data measures variations in the intensity of the Earth's magnetic field caused by the contrasting content of rock-forming minerals in the Earth crust. Magnetic anomalies can be either positive (field stronger than normal) or negative (field weaker) depending on the susceptibility of the rock. The data are processed via standard methods to ensure the response recorded is that due only to the rocks in the ground. The results produce datasets that can be interpreted to reveal the geological structure of the sub-surface. The processed data is checked for quality by GA geophysicists to ensure that the final data released by GA are fit-for-purpose. . This Lake Eyre - TMI RTP 1VD grid (AWAGS) is the first vertical derivative of the TMI RTP grid of the Lake Eyre Airborne Magnetic Radiometric and DEM survey, SA, 2017 survey. This grid has a cell size of 0.0004 degrees (approximately 41m) , and given in units of nT per metre (nT/m). The data used to produce the TMI grid was acquired in 2017 by the SA Government, and consisted of 92215 line-kilometres of data at 200m line spacing and 60m terrain clearance. The data has had a variable reduction to the pole applied to centre the magnetic anomaly over the magnetised body. The VRTP processing followed a differential reduction to pole calculation up to 5th order polynomial. Magnetic inclination and declination were derived from the IGRF-11 geomagnetic reference model using a data representative date and elevation representative of the survey. A first vertical derivative was calculated by applying a fast Fourier transform (FFT) process to the TMI RTP grid of the Lake Eyre Airborne Magnetic Radiometric and DEM survey, SA, 2017 survey to produce this grid. This grid was calculated using an algorithm from the INTREPID Geophysics software package. This grid shows the magnetic response of subsurface features with contrasting magnetic susceptibilities. The grid can also be used to locate structural features such as dykes.
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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.
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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.