940 resultados para Remote Australia


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Murray Valley encephalitis (MVE) virus is a mosquito-borne flavivirus causing severe encephalitis with a resultant high morbidity and mortality. In the period 1989-1993. we undertook a cross-sectional and longitudinal studs by annually screening members of a small remote Aboriginal community in northwestern Australia for MVE virus antibodies. Of the estimated 250-300 people in the community. 249 were tested, and 52.6% had positive serology to MVE. The proportion testing positive increased with increasing age group. and males were slightly more likely to be positive than females. During the study period. a high proportion of the population seroconverted to MVE: the clinical/subclinical ratio seems to be lower than previously reported. Although MVE is mostly asymptomatic, the devastating consequences of clinical illness indicate that advice should be provided regarding the avoidance of mosquito bites. Our longitudinal study showed that the risk of seroconversion was similar for each age group. not just the young.

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Understanding the ecological role of benthic microalgae, a highly productive component of coral reef ecosystems, requires information on their spatial distribution. The spatial extent of benthic microalgae on Heron Reef (southern Great Barrier Reef, Australia) was mapped using data from the Landsat 5 Thematic Mapper sensor. integrated with field measurements of sediment chlorophyll concentration and reflectance. Field-measured sediment chlorophyll concentrations. 2 ranging from 23-1.153 mg chl a m(2), were classified into low, medium, and high concentration classes (1-170, 171-290, and > 291 mg chl a m(-2)) using a K-means clustering algorithm. The mapping process assumed that areas in the Thematic Mapper image exhibiting similar reflectance levels in red and blue bands would correspond to areas of similar chlorophyll a levels. Regions of homogenous reflectance values corresponding to low, medium, and high chlorophyll levels were identified over the reef sediment zone by applying a standard image classification algorithm to the Thematic Mapper image. The resulting distribution map revealed large-scale ( > 1 km 2) patterns in chlorophyll a levels throughout the sediment zone of Heron Reef. Reef-wide estimates of chlorophyll a distribution indicate that benthic Microalgae may constitute up to 20% of the total benthic chlorophyll a at Heron Reef. and thus contribute significantly to total primary productivity on the reef.

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Background: There has been a proliferation of quality use of medicines activities in Australia since the 1990s. However, knowledge of the nature and extent of these activities was lacking. A mechanism was required to map the activities to enable their coordination. Aims: To develop a geographical mapping facility as an evaluative tool to assist the planning and implementation of Australia's policy on the quality use of medicines. Methods: A web-based database incorporating geographical mapping software was developed. Quality use of medicines projects implemented across the country was identified from project listings funded by the Quality Use of Medicines Evaluation Program, the National Health and Medical Research Council, Mental Health Strategy, Rural Health Support, Education and Training Program, the Healthy Seniors Initiative, the General Practice Evaluation Program and the Drug Utilisation Evaluation Network. In addition, projects were identified through direct mail to persons working in the field. Results: The Quality Use of Medicines Mapping Project (QUMMP) was developed, providing a Web-based database that can be continuously updated. This database showed the distribution of quality use of medicines activities by: (i) geographical region, (ii) project type, (iii) target group, (iv) stakeholder involvement, (v) funding body and (vi) evaluation method. At September 2001, the database included 901 projects. Sixty-two per cent of projects had been conducted in Australian capital cities, where approximately 63% of the population reside. Distribution of projects varied between States. In Western Australia and Queensland, 36 and 73 projects had been conducted, respectively, representing approximately two projects per 100 000 people. By comparison, in South Australia and Tasmania approximately seven projects per 100 000 people were recorded, with six per 100 000 people in Victoria and three per 100 000 people in New South Wales. Rural and remote areas of the country had more limited project activity. Conclusions: The mapping of projects by geographical location enabled easy identification of high and low activity areas. Analysis of the types of projects undertaken in each region enabled identification of target groups that had not been involved or services that had not yet been developed. This served as a powerful tool for policy planning and implementation and will be used to support the continued implementation of Australia's policy on the quality use of medicines.

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A book about remote labs and engineering education begs to begin with the question, “Why do engineering programs include lab work?” Although this may seem like a given and not worth discussing, whenever we’re faced with innovative ideas, it’s important to “put everything on the table” in order to reassess its value to our program or goals. What is it about lab work that is of value to students? Are there elements of traditional labs that we could let go of? Are there elements that we don’t want to lose? These questions can help us to clarify how and why labs are integrated into an engineering education program.

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The Soil Moisture and Ocean Salinity (SMOS) satellite marks the commencement of dedicated global surface soil moisture missions, and the first mission to make passive microwave observations at L-band. On-orbit calibration is an essential part of the instrument calibration strategy, but on-board beam-filling targets are not practical for such large apertures. Therefore, areas to serve as vicarious calibration targets need to be identified. Such sites can only be identified through field experiments including both in situ and airborne measurements. For this purpose, two field experiments were performed in central Australia. Three areas are studied as follows: 1) Lake Eyre, a typically dry salt lake; 2) Wirrangula Hill, with sparse vegetation and a dense cover of surface rock; and 3) Simpson Desert, characterized by dry sand dunes. Of those sites, only Wirrangula Hill and the Simpson Desert are found to be potentially suitable targets, as they have a spatial variation in brightness temperatures of <4 K under normal conditions. However, some limitations are observed for the Simpson Desert, where a bias of 15 K in vertical and 20 K in horizontal polarization exists between model predictions and observations, suggesting a lack of understanding of the underlying physics in this environment. Subsequent comparison with model predictions indicates a SMOS bias of 5 K in vertical and 11 K in horizontal polarization, and an unbiased root mean square difference of 10 K in both polarizations for Wirrangula Hill. Most importantly, the SMOS observations show that the brightness temperature evolution is dominated by regular seasonal patterns and that precipitation events have only little impact.

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Leading patterns of observed monthly extreme rainfall variability in Australia are examined using an Empirical Orthogonal Teleconnection (EOT) method. Extreme rainfall variability is more closely related to mean rainfall variability during austral summer than in winter. The leading EOT patterns of extreme rainfall explain less variance in Australia-wide extreme rainfall than is the case for mean rainfall EOTs. We illustrate that, as with mean rainfall, the El Niño-Southern Oscillation (ENSO) has the strongest association with warm-season extreme rainfall variability, while in the cool-season the primary drivers are atmospheric blocking and the subtropical ridge. The Indian Ocean Dipole and Southern Annular Mode also have significant relationships with patterns of variability during austral winter and spring. Leading patterns of summer extreme rainfall variability have predictability several months ahead from Pacific sea surface temperatures (SSTs) and as much as a year in advance from Indian Ocean SSTs. Predictability from the Pacific is greater for wetter than average summer months than for months that are drier than average, whereas for the Indian Ocean the relationship has greater linearity. Several cool-season EOTs are associated with mid-latitude synoptic-scale patterns along the south and east coasts. These patterns have common atmospheric signatures denoting moist onshore flow and strong cyclonic anomalies often to the north of a blocking anti-cyclone. Tropical cyclone activity is observed to have significant relationships with some warm season EOTs. This analysis shows that extreme rainfall variability in Australia can be related to remote drivers and local synoptic-scale patterns throughout the year.

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Sargassum C. Agardh is one of the most diverse genera of marine macro-algae and commonly inhabits shallow tropical and sub-tropical waters. This study aimed to investigate the effect of seasonality and the associated water quality changes on the distribution, canopy cover, mean thallus length and the biomass of Sargassum beds around Point Peron, Shoalwater Islands Marine Park, Southwest Australia. Samples of Sargassum and seawater were collected every three months from summer 2012 to summer 2014 from four different reef zones. A combination of in situ observations and WorldView-2 satellite remote-sensing images were used to map the spatial distribution of Sargassum beds and other associated benthic habitats. The results demonstrated a strong seasonal variation in the environmental parameters, canopy cover, mean thallus length, and biomass of Sargassum, which were significantly (P < 0.05) influenced by the nutrient concentration (PO43-, NO3-, NH4+) and rainfall. However, no variation in any studied parameter was observed among the four reef zones. The highest Sargassum biomass peaks occurred between late spring and early summer (from September to January). The results provide essential information to guide effective conservation and management, as well as sustainable utilisation of this coastal marine renewable resource.

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Bathurst Harbour in World Heritage southwest Tasmania, Australia, is one of the world’s most pristine estuarine systems. At present there is a lack of data on pollution impacts or long-term natural variability in the harbor. A ca. 350-year-old 210Pb-dated sediment core was analysed for trace metals to track pollution impacts from local and long-range sources. Lead and antimony increased from AD 1870 onwards, which likely reflects remote (i.e. mainland Australian and global) atmospheric pollution sources. Variability in the concentrations of copper and zinc closely followed the history of mining activities in western Tasmania, which began in the AD 1880s. Tin was generally low throughout the core, except for a large peak in AD 1989 ± 0.5 years, which may be a consequence of input from a local small-scale alluvial tin mine. Changes in diatom assemblages were also investigated. The diatom flora was composed mostly of planktonic freshwater and benthic brackish-marine species, consistent with stratified estuarine conditions. Since mining began, however, an overall decrease in the proportion of planktonic to benthic taxa occurred, with the exception of two distinct peaks in the twentieth century that coincided with periods of high rainfall. Despite the region’s remoteness, trace metal analyses revealed evidence of atmospheric pollution from Tasmanian and possibly longer-range mining activities. This, together with recent low rainfall, appears to have contributed to altering the diatom assemblages in one of the most pristine temperate estuaries in the world.

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The spatial and temporal dynamics of seagrasses have been well studied at the leaf to patch scales, however, the link to large spatial extent landscape and population dynamics is still unresolved in seagrass ecology. Traditional remote sensing approaches have lacked the temporal resolution and consistency to appropriately address this issue. This study uses two high temporal resolution time-series of thematic seagrass cover maps to examine the spatial and temporal dynamics of seagrass at both an inter- and intra-annual time scales, one of the first globally to do so at this scale. Previous work by the authors developed an object-based approach to map seagrass cover level distribution from a long term archive of Landsat TM and ETM+ images on the Eastern Banks (~200 km**2), Moreton Bay, Australia. In this work a range of trend and time-series analysis methods are demonstrated for a time-series of 23 annual maps from 1988 to 2010 and a time-series of 16 monthly maps during 2008-2010. Significant new insight was presented regarding the inter- and intra-annual dynamics of seagrass persistence over time, seagrass cover level variability, seagrass cover level trajectory, and change in area of seagrass and cover levels over time. Overall we found that there was no significant decline in total seagrass area on the Eastern Banks, but there was a significant decline in seagrass cover level condition. A case study of two smaller communities within the Eastern Banks that experienced a decline in both overall seagrass area and condition are examined in detail, highlighting possible differences in environmental and process drivers. We demonstrate how trend and time-series analysis enabled seagrass distribution to be appropriately assessed in context of its spatial and temporal history and provides the ability to not only quantify change, but also describe the type of change. We also demonstrate the potential use of time-series analysis products to investigate seagrass growth and decline as well as the processes that drive it. This study demonstrates clear benefits over traditional seagrass mapping and monitoring approaches, and provides a proof of concept for the use of trend and time-series analysis of remotely sensed seagrass products to benefit current endeavours in seagrass ecology.

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Long term global archives of high-moderate spatial resolution, multi-spectral satellite imagery are now readily accessible, but are not being fully utilised by management agencies due to the lack of appropriate methods to consistently produce accurate and timely management ready information. This work developed an object-based remote sensing approach to map land cover and seagrass distribution in an Australian coastal environment for a 38 year Landsat image time-series archive (1972-2010). Landsat Multi-Spectral Scanner (MSS), Thematic Mapper (TM) and Enhanced Thematic Mapper (ETM+) imagery were used without in situ field data input (but still using field knowledge) to produce land and seagrass cover maps every year data were available, resulting in over 60 map products over the 38 year archive. Land cover was mapped annually using vegetation, bare ground, urban and agricultural classes. Seagrass distribution was also mapped annually, and in some years monthly, via horizontal projected foliage cover classes, sand and deep water. Land cover products were validated using aerial photography and seagrass maps were validated with field survey data, producing several measures of accuracy. An average overall accuracy of 65% and 80% was reported for seagrass and land cover products respectively, which is consistent with other studies in the area. This study is the first to show moderate spatial resolution, long term annual changes in land cover and seagrass in an Australian environment, created without the use of in situ data; and only one of a few similar studies globally. The land cover products identify several long term trends; such as significant increases in South East Queensland's urban density and extent, vegetation clearing in rural and rural-residential areas, and inter-annual variation in dry vegetation types in western South East Queensland. The seagrass cover products show that there has been a minimal overall change in seagrass extent, but that seagrass cover level distribution is extremely dynamic; evidenced by large scale migrations of higher seagrass cover levels and several sudden and significant changes in cover level. These mapping products will allow management agencies to build a baseline assessment of their resources, understand past changes and help inform implementation and planning of management policy to address potential future changes.

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A simple method for efficient inversion of arbitrary radiative transfer models for image analysis is presented. The method operates by representing the shape of the function that maps model parameters to spectral reflectance by an adaptive look-up tree (ALUT) that evenly distributes the discretization error of tabulated reflectances in spectral space. A post-processing step organizes the data into a binary space partitioning tree that facilitates an efficient inversion search algorithm. In an example shallow water remote sensing application, the method performs faster than an implementation of previously published methodology and has the same accuracy in bathymetric retrievals. The method has no user configuration parameters requiring expert knowledge and minimizes the number of forward model runs required, making it highly suitable for routine operational implementation of image analysis methods. For the research community, straightforward and robust inversion allows research to focus on improving the radiative transfer models themselves without the added complication of devising an inversion strategy.

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Approximately 18,400 km**2 of seagrass habitat has been mapped within the coastal waters (<15 m) of Queensland (Australia) between November 1984 and June 2010. The total seagrass meadow distribution was calculated by merging maps from 115 separate mapping surveys (varying locations and dates). Due to tropical seagrass dynamism, meadow distribution can change seasonally and between years, and as a consequence, the composite represents the maximum area of seabed where seagrass has been observed/recorded. Mapping survey methodologies followed standardised global seagrass research methods (McKenzie et al. 2001) where the presence of seagrass was determined from in situ visual assessment of the seabed by either divers or drop cameras at GPS marked positions. Seagrass meadow boundaries were determined based on the positions of survey sites and the presence of seagrass, coupled with depth contours and remote sensing (e.g. aerial photography) where available. The merged meadow boundary accuracy was dependent on the original survey maps and varied from 10-100 m. The resulting composite seagrass distribution was saved as an ArcMap polygon shapefile, and projected to Geocentric Datum of Australia GDA94.

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The spatial and temporal dynamics of seagrasses have been studied from the leaf to patch (100 m**2) scales. However, landscape scale (> 100 km**2) seagrass population dynamics are unresolved in seagrass ecology. Previous remote sensing approaches have lacked the temporal or spatial resolution, or ecologically appropriate mapping, to fully address this issue. This paper presents a robust, semi-automated object-based image analysis approach for mapping dominant seagrass species, percentage cover and above ground biomass using a time series of field data and coincident high spatial resolution satellite imagery. The study area was a 142 km**2 shallow, clear water seagrass habitat (the Eastern Banks, Moreton Bay, Australia). Nine data sets acquired between 2004 and 2013 were used to create seagrass species and percentage cover maps through the integration of seagrass photo transect field data, and atmospherically and geometrically corrected high spatial resolution satellite image data (WorldView-2, IKONOS and Quickbird-2) using an object based image analysis approach. Biomass maps were derived using empirical models trained with in-situ above ground biomass data per seagrass species. Maps and summary plots identified inter- and intra-annual variation of seagrass species composition, percentage cover level and above ground biomass. The methods provide a rigorous approach for field and image data collection and pre-processing, a semi-automated approach to extract seagrass species and cover maps and assess accuracy, and the subsequent empirical modelling of seagrass biomass. The resultant maps provide a fundamental data set for understanding landscape scale seagrass dynamics in a shallow water environment. Our findings provide proof of concept for the use of time-series analysis of remotely sensed seagrass products for use in seagrass ecology and management.

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This paper describes seagrass species and percentage cover point-based field data sets derived from georeferenced photo transects. Annually or biannually over a ten year period (2004-2015) data sets were collected using 30-50 transects, 500-800 m in length distributed across a 142 km**2 shallow, clear water seagrass habitat, the Eastern Banks, Moreton Bay, Australia. Each of the eight data sets include seagrass property information derived from approximately 3000 georeferenced, downward looking photographs captured at 2-4 m intervals along the transects. Photographs were manually interpreted to estimate seagrass species composition and percentage cover (Coral Point Count excel; CPCe). Understanding seagrass biology, ecology and dynamics for scientific and management purposes requires point-based data on species composition and cover. This data set, and the methods used to derive it are a globally unique example for seagrass ecological applications. It provides the basis for multiple further studies at this site, regional to global comparative studies, and, for the design of similar monitoring programs elsewhere.