environmental
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Robust methods for generating spatially continuous data from point locations of physical seabed properties are essential for accurate biodiversity prediction. For many national-scale applications, spatially continuous seabed sediment data are typically derived from sparsely and unevenly distributed point locations, particularly in the deep ocean due to the expense and practical limitations of acquiring samples. Methods for deriving spatially continuous data are usually data- and variable-specific making it difficult to select an appropriate method for any given physical seabed property. To improve the spatial modelling of physical seabed properties, this study compared the results of a variety of methods for deriving spatially continuous mud content data for the southwest margin of Australia (523,400 km2) based on 177 sparsely and unevenly distributed point samples. For some methods, secondary variables were also used in the analysis, including: bathymetry, distance-to-coast, seabed slope, and geomorphic province (i.e., shelf, slope, etc.). Effects of sample density were also investigated. The predictive performance of the methods was assessed using a 10-fold cross validation and visual examination. A combined method (random forest and ordinary kriging: RFrf) proved the most accurate method, with an RMAE up to 17% less than the control. No threshold sample density was detected; as sample density increased so did the accuracy of the method. The RMAE of the most accurate method is about 30% lower than that of the best methods in previous publications, further highlighting the robustness of the method developed in this study. The results of this study show that significant improvements in the accuracy of the spatially continuous seabed properties can be achieved through the application of an appropriate interpolation method. The outcomes of this study can be applied to the modelling of a wide range of physical properties for improved marine biodiversity prediction.
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The Vlaming Basin survey (GA survey # 334) was conducted under the Nation CO2 Infrastructure Plan (NCIP) on the MV Southern Supporter between the 12th to the 21st April 2012, with technical support provided from Fugro. The survey obtained geological (sedimentological, geochemical, geophysical) and biological data to support assessments of offshore acerage release areas for potential CO2 storage, and investigate the relationships between the physical environment and associated biota for biodiversity prediction. The purpose of the survey was to contribute to the assessment of the Vlaming Sub-basin for containment risk, as well as complement and validate interpretation of seismic data in the same area. Underwater footage was collected from 12 stations, although quality varies between sites and among transect lines. Video folders are named according to station number, followed by gear code (CAM=Ray Tech Underwater system), then the deployment number, and followed by region. For example, 12CAM02_D1 represents a video transect from station 12 that was the 2nd video transect of the survey and located in area region D1. Multiple files within transect folders indicate location metadata (Start of line location). In addition, USBL (Ultra-short baseline) text files located in the same folder provide continuous navigational information on location, time (UTC) and depth of each video transect line.
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From 1995 to 2000 information from the federal and state governments was compiled for Comprehensive Regional Assessments (CRA), which formed the basis for Regional Forest Agreements (RFA) that identified areas for conservation to meet targets agreed by the Commonwealth Government with the United Nations. This CD was created as part of GA's contribution to the East Gippsland CRA. It contains final versions of all data coverages and shapefiles, AMLs and Graphics files in ArcInfo (.gra), postscript (.ps) and Web ready (.gif) formats, and final versions of documents, maps and figures submitted for publishing.
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This map was produced due to a request from DFAT (No. 431) for a map similar to the one produced last year. The extents of the whaling zone were supplied by DFAT.
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This record gives a brief account of the conditions encountered in a geological reconnaissance of the south-western portion of the Canning Basin - an area covered mostly by sand and seif dune, interspersed by scattered low rock outcrops.
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This report contains the preliminary results of Geoscience Australia survey 273 to northwest Torres Strait. This survey was undertaken as part of a research program within the Torres Strait CRC aimed at understanding marine biophysical processes in Torres Strait and their effect on seagrass habitats. Two Geoscience Australia surveys were undertaken as part of this program, survey 266 measured monsoon season conditions (Heap et al., 2005), and survey 273 measured trade wind conditions. Section 6 compares and contrasts the survey results acquired for both surveys. Section 7 addresses the results of the survey program in light of the objectives of the CRC proposal. Survey 273 acquired numerous different data types to assist with characterising the mobile sediments and hydrodynamic nature of the region. Multibeam sonar, current meters, grab samples, vibro-cores, underwater video, meteorological data (from the Bureau of Meteorology), Landsat imagery, were all used to characterise the seabed hydrodynamics of Torres Strait.
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A soil geochemical dataset (major and minor elements) is provided for NE-Brazil, based on low-density sampling. It covers an area from about 2°S to 12°S, and from 34°W to 49°W (ca. 1.7 million km2), and includes top (TOP: 0-20 cm) and bottom (BOT: 30-50 cm) mineral soil materials. Results are put in perspective using two recent and comparable studies, the National Geochemical Survey of Australia (NGSA) and the European Geochemical Mapping of Agricultural Soils (GEMAS). Cluster analysis revealed similar results for TOP and BOT samples, yielding three groups of elements/oxides displaying similar behaviour: Gr.1 comprising Al2O3, Fe2O3, TiO2, and P2O5; Gr.2 comprising CaO, K2O, MgO, MnO, and Na2O; and Gr.3 being SiO2. All median element concentrations in the Brazilian samples are depleted compared to World Soil Averages (WSA), except for Al2O3 and SiO2, which are respectively similar to WSA and enriched in Brazil. While this depletion is moderate for Fe2O3, MnO, P2O5, and TiO2, it reaches an order of magnitude and more for K2O, MgO, CaO, and Na2O. The difference between TOP and BOT concentrations is lower than the variation of either TOP or BOT concentrations between sample sites. Similar spatial distribution and the high correlation between TOP and BOT concentrations suggest that (1) similar processes and parameters are of general relevance for the geochemical composition of TOP and BOT samples, and (2) topsoil and subsoil in the investigation area are not decoupled. Weathering indicators are significantly and positively correlated and show similar spatial distributions in TOP and BOT samples. All elements deliver similar mass removal times (time to export all material from a 10 cm soil layer) and clearly discern between the regions: Europe with the fastest "depletion" (12,200 ± 300 years), followed by Australia (33,200 ± 3,000 years) and Brazil (86,700 ± 3,000 years). Similar results emerge when calculating denudation rates, using independent fluvial .../...
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This Summary Report provides an overview of the Regional Hydrogeological Characterisation of the Laura Basin, Queensland, Technical Report (GeoCat number 78881).
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Machine learning methods, like random forest (RF), have shown their superior performance in various disciplines, but have not been previously applied to the spatial interpolation of environmental variables. In this study, we compared the performance of 23 methods, including RF, support vector machine (SVM), ordinary kriging (OK), inverse distance squared (IDS), and their combinations (i.e., RFOK, RFIDS, SVMOK and SVMIDS), using mud content samples in the southwest Australian margin. We also tested the sensitivity of the combined methods to input variables and the accuracy of averaging predictions of the most accurate methods. The accuracy of the methods was assessed using a 10-fold cross-validation. The spatial patterns of the predictions of the most accurate methods were also visually examined for their validity. This study confirmed the effectiveness of RF, especially its combination with OK or IDS, and also confirmed the sensitivity of RF and its combined methods to the input variables. Averaging the predictions of the most accurate methods showed no significant improvement in the predictive accuracy. Visual examination proved to be an essential step in assessing the spatial predictions. This study has opened an alternative source of methods for spatial interpolation of environmental properties.
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In this study, we conducted a simulation experiment to identify robust spatial interpolation methods using samples of seabed mud content in the Geoscience Australian Marine Samples database. Due to data noise associated with the samples, criteria are developed and applied for data quality control. Five factors that affect the accuracy of spatial interpolation were considered: 1) regions; 2) statistical methods; 3) sample densities; 4) searching neighbourhoods; and 5) sample stratification. Bathymetry, distance-to-coast and slope were used as secondary variables. Ten-fold cross-validation was used to assess the prediction accuracy measured using mean absolute error, root mean square error, relative mean absolute error (RMAE) and relative root mean square error. The effects of these factors on the prediction accuracy were analysed using generalised linear models. The prediction accuracy depends on the methods, sample density, sample stratification, search window size, data variation and the study region. No single method performed always superior in all scenarios. Three sub-methods were more accurate than the control (inverse distance squared) in the north and northeast regions respectively; and 12 sub-methods in the southwest region. A combined method, random forest and ordinary kriging (RKrf), is the most robust method based on the accuracy and the visual examination of prediction maps. This method is novel, with a relative mean absolute error (RMAE) up to 17% less than that of the control. The RMAE of the best method is 15% lower in two regions and 30% lower in the remaining region than that of the best methods in the previously published studies, further highlighting the robustness of the methods developed. The outcomes of this study can be applied to the modelling of a wide range of physical properties for improved marine biodiversity prediction. The limitations of this study are discussed. A number of suggestions are provided for further studies.