ALOS PALSAR DATA (Level 1.1-1.5) DATA FORMAT (Revision I) from Remote Sensing Technology Center of Japan.

Ingest Method for UNIX Operators of Data from ACRES CCRS (BIL) Tapes. ACRES Technical Document, Version: 2, October 1997

Increased loads of land-based pollutants associated with land use change are a major threat to coastal-marine ecosystems globally. Identifying the affected areas and the scale of influence on marine ecosystems is critical to assess the ecological impacts of degraded water quality and to inform planning for catchment management and marine conservation. Studies using remotely-sensed data have contributed to our understanding of the occurrence and extent of influence of river plumes, as well as to assess exposure of ecosystems to river-borne pollutants. However, refinement of plume modelling techniques is required to improve risk assessments. We developed a novel approach to model exposure of coastal-marine ecosystems to river-borne pollutants. The model is based on supervised classification of true-colour satellite imagery to map the extent of plumes and to qualitatively assess the dispersal of pollutants in plumes. We use the Great Barrier Reef (GBR) to test our approach. We combined frequency of plume occurrence with spatially-distributed loads (based on a cost-distance function) to create maps of exposure to suspended sediment and dissolved inorganic nitrogen. We then compared annual exposure maps (2007-2011) to assess inter-annual variability in the exposure of coral reefs and seagrass beds. Our findings indicate that classification of true colour satellite images is useful to map plumes and to qualitatively assess exposure to river-borne pollutants. This approach should be considered complementary to remote sensing methods based on ocean colour products used to characterise surface water in plumes. The proposed exposure model is useful to study the spatial and temporal variation in exposure of coastal-marine ecosystems to riverine plumes. Observed inter-annual variation in exposure of habitats to pollutants stresses the need to incorporate the temporal component in exposure and risk models.

The Timor Sea airborne laser fluorosensor (ALF) MkII survey was flown in 1994 by BP over the western Bonaparte Basin (Vulcan Sub-basin and Nancar Trough). 65 lines were acquired at 5,000m spacing in a NW-SE orientation. A total of 436,972 spectra were collected at an average spacing of 14.9m to 20.3m. Each recorded spectrum is the average of ten detected spectra. The strongest fluors found on the Timor Sea MkII ALF survey were located over the Jabiru Field. The anomalous intensity and distribution of these fluors suggests they are related to the field development rather than natural oil seepage. Apart from the Jabiru anomaly, the fluorescence response was very weak over the rest of the survey area. The 5km line spacing is not sufficiently close to detect most fluor clusters. The line spacing of 300m used on the nearby MkIII ALF survey over the Skua field was only just sufficient to detect the fluor cluster lying near that accumulation. The Timor Sea ALF MkII data is probably not suitable for identifying the isolated, low intensity fluors usually detectable on ALF MkII surveys in the region.

ALOS Product Format Description (PALSAR Level 1.0) This document describes a format description of PALSAR Level 1.0 data generated by ALOS Data Processing Subsystem. PALSAR Level 1.0 data is processed from PALSAR Level 0 data distributed by ALOS Central Information Subsystem. PALSAR Level 1.0 products are defined by scene ID and scene shift using orbit frame number.

Airborne laser fluorosensor (ALF) data was acquired by BP in Australia between 1990 and 1991 and originally processed by BP immediately after acquisition. The data was reprocessed by Signalworks Pty Ltd in 2000 and 2001 with significant differences in the number of fluors picked and oil seepage distributions interpreted. A comparison of the data processing and interpretation techniques shows similar methods used by both companies. The main difference with the Signalworks interpretation is that a larger number of lower confidence fluors are used to determine the seepage distributions. Low signal to noise ratio (S/N) in the MkII ALF data used is the main cause of data analysis difficulties and differences between interpretations. This problem was greatly reduced in the MkIII system developed later which results in more consistent interpretations between companies. Another problem with ALF surveys covering very large areas is the pattern of background fluorescence / Raman area ratios (F/R) that can affect the pattern of picked fluors. Both BP and Signalworks use an averaging technique to determine the background F/R levels over the survey. The BP analysis has usually resulted in very few picked fluors and little information about seepage patterns. The Signalworks analysis has attempted to pick sufficient fluors to define seepage patterns but is affected by the low S/N and background F/R patterns.

ABSTRACT Oil and gas reservoirs below the sea bed can experience seepage through the seafloor, resulting in oil slicks on the sea surface. Remote sensing has the potential to cost-effectively scan extensive and/or remote regions for such surface slicks, thereby identifying areas in producing and frontier basins that are prospective for hydrocarbons. Accordingly, we are developing a two-pronged, remote sensing-based approach for seepage slick studies in the Australian Marine Jurisdiction (AMJ): 1) Detection : building a semi-automated processing and classification system in order to scan large numbers of SAR scenes for potential natural slick targets. This includes time series analysis. 2) Identification : investigating the potential of optical remote sensing as a diagnostic tool for further, targeted study. Here, we present the.....

Salt lakes, also known as playa lakes, are a common feature of the Australian landscape, and are a strong indicator of our current and past climates. Despite their abundance they have not been extensively studied in Australia, with little research undertaken since the early benchmark work of the 1970s - 1980s (e.g. Bowler, 1971, 1981) which largely focussed on geomorphologic evolutionary patterns and trends. Notwithstanding, salt lakes contain some of the highest levels biological endemism in Australia (DeDecker, 1983) and their unique, and commonly extreme, chemistry offers the potential for distinctive saline mineralisation and potentially economic concentrations of Li, K, B, REEs, Br and U (e.g. Butt et al. 1984; Nissenbaum, 1993; Orris, 2011).

A multi-agency collaboration between Australian government partners has been working towards making continent-scale, public, web-accessible and GIS-compatible ASTER geoscience maps. CSIRO along with Geoscience Australia and several state government agencies, (including GSWA, GSQ, DMITRE and NTGS), have developed methodology and produced 15 geoscientific products, with applications for mineral mapping and exploration, soil-mapping, environment and agricultural sectors. This work represents the largest ASTER mosaic of this type in the world and sets a new benchmark for state-to-continent scale spectral remote sensing. The project is supported both nationally and internationally by the ASTER Science Team, ERSDAC, NASA and the USGS. Outcomes include the formation of a platform for establishing national standards; geoscience product nomenclature; processing methods; accuracy assessments; and traceable documentation. Detailed product notes outline these standards and provide significant knowledge transfer for existing and new users of this type of data. Hyperion satellite hyperspectral imagery has been critical for calibration and validation of the processed ASTER data, reduction to 'surface' reflectance using independent validation data such as Hyperion, and calculating statistics to generate regression coefficients, reduces errors in the ASTER instrument and increases reliability and corroboration of spectral responses.

Normalising for atmospheric, land surface bidirectional reflectance distribution function (BRDF) and terrain illumination effects are essential in satellite data processing. It is important both for a single scene when the combination of land cover, sun, view angles and terrain slope angles create anisotropy and for multiple scenes in which the sun angle changes. Geoscience Australia (GA) is establishing a procedure to conduct physically based atmospheric BRDF and terrain illumination correction for moderate spatial resolution satellite imagery (10-100 m) such as Landsat using a coupled atmospheric and BRDF model. In particular, the method is not dependent on the image data, does not need extensive field data, can be applied equally to different environments and used with different sensors in a consistent way. Furthermore, the corrected surface reflectance derived using this method can be used to calibrate and cross-calibrate satellite sensors. More importantly, the normalized reflectance can be used for time series analysis to trace climate change and land cover variation using multiple sensors (including satellite, airborne and ground based). In this paper, we will describe the algorithm being progressed at GA. Preliminary results from the algorithm will be compared with ground based reflectance measurements for selected validation sites. The paper will also discuss how the environmental input data for the model, such as aerosol, water vapour and BRDF parameters are selected and applied.