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Understanding housing preferences is critical for successful compact city development. However, there is limited research on understanding preference heterogeneity in dwelling type choices. Using the Household Income and Labour Dynamics in Australia survey, this paper identifies the key housing and built environment characteristics associated with changes in dwelling type choice from detached houses to high-density. A latent class choice model captures the heterogeneity of dwelling type preferences within a traditionally low-density city, Brisbane, Australia. Findings reveal six household classes with distinct dwelling preferences: Class 1 (senior households without children with other family members) and Class 2 (couple families with children) in inner-city areas, Class 3 (high-income young households) and Class 4 (low-income households without children) in middle-city areas, Class 5 (low-income families with children) and Class 6 (middle-income young families without children) in outer-city areas. Residential environments with better access to educational facilities encourage Classes 3 and 6 to change to high-density living. Greater land use diversity encourages Classes 2, 3, and 6 to move towards high-density living. The findings can be used to design and improve high-density housing for targeted population groups across inner-, middle- and outer-city areas. [ABSTRACT FROM AUTHOR]

Grassland fires are a serious problem in Victoria, Australia due to large quantity of dry grass. Grassland curing degree (GCD) measures the dryness of the grass and is an important factor for assessing grassland fire danger. Grassland curing maps (GCMs) display the spatial distribution of GCDs, but the quality of GCMs varies depending on the spatial resolution of the observing satellite remote sensing system. The higher the spatial resolution, the finer the GCD details and more spatial variations the GCM can reveal. In this study, GCD calculation algorithm named MapVictoria based on MODIS data is tested for Landsat 8 Sentinel 2; GCMs generated from these three satellites are contrasted by their GCD differences, defined here as inter-satellite variability (ISV). ISV is used to identify areas where higher resolution satellite GCMs should be used. Results show that spatial resolution difference (ΔSR), seasonality and geographical locations affect the magnitude of the ISV. Based on these findings, this paper provides recommendations to decision makers on where and when to use which satellite for grassland observations. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to contribute to the definition of a European perspective on Digital Earth (DE), identify some actions that can contribute to raise the awareness of DE in the European context and thus strengthen the European contribution to the International Society for Digital Earth (ISDE). The paper identifies opportunities and synergies with the current policy priorities in Europe (Europe 2020, Innovation Union and Digital Agenda) and highlights a number of key areas to advance the development of DE from a European perspective: (1) integrating scientific research into DE; (2) exploiting the Observation Web with human-centred sensing; and (3) governance, including the establishment of stronger linkages across the European landscape of funding streams and initiatives. The paper is offered also as a contribution to the development of this new vision of DE to be presented at the next International DE Conference in Perth, Australia, in August 2011. The global recognition of this new vision will then reinforce the European component and build a positive feedback loop for the further implementation of DE across the globe. [ABSTRACT FROM AUTHOR]

A vital component of fire detection from remote sensors is the accurate estimation of the background temperature of an area in fire's absence, assisting in identification and attribution of fire activity. New geostationary sensors increase the data available to describe background temperature in the temporal domain. Broad area methods to extract the expected diurnal cycle of a pixel using this temporally rich data have shown potential for use in fire detection. This paper describes an application of a method for priming diurnal temperature fitting of imagery from the Advanced Himawari Imager. The BAT method is used to provide training data for temperature fitting of target pixels, to which thresholds are applied to detect thermal anomalies in 4 μm imagery over part of Australia. Results show the method detects positive thermal anomalies with respect to the diurnal model in up to 99% of cases where fires are also detected by Low Earth Orbiting (LEO) satellite active fire products. In absence of LEO active fire detection, but where a burned area product recorded fire-induced change, this method also detected anomalous activity in up to 75% of cases. Potential improvements in detection time of up to 6 h over LEO products are also demonstrated. [ABSTRACT FROM AUTHOR]

Virtual Australia is not a well-defined or agreed concept. This discussion paper conceptualises Virtual Australia as a 'virtual [digital] model containing and representing all non-trivial objects and their contextual environment - from blue sky to bedrock - in real world Australia'. It describes a scenario for Virtual Australia, one or two decades from now, in which the locations and conditions of non-trivial objects and their environment are updated automatically through a combination of remote sensing and wireless communication technologies in support of a 'Supranet'. It then examines the concept of the 'Supranet' - a pervasive information network based largely on wireless technology linking the physical world to a virtual model in real time - and develops and discusses three principal characteristics of the Supranet: the ability to have or collect specific information (know); the ability to process information (think); and the ability to communicate that information. If, in the near future, any or all non-trivial devices are to some extent able to know, think and communicate, the potential for object autonomy will be realised. [ABSTRACT FROM AUTHOR]

To comprehensively support national and international initiatives for sustainable development, land cover products need to be reliably and routinely generated within operational frameworks. Coupled with consistent semantics and taxonomies, ensuring confidence in mapping land cover for multiple time periods, facilitates informed decision-making at scales appropriate to multiple policy domains. The United Nations Food and Agriculture Organisation (FAO) Land Cover Classification System (LCCS) provides a taxonomy that comparable at different scales, level of detail and geographic location. The Open Data Cube (ODC) initiative offers a framework for operational continental-scale land cover mapping using analysis-ready Earth Observation data. This study utilised the FAO LCCS framework and the Landsat sensor data through Digital Earth Australia (DEA; Australia's ODC instance) to generate consistent and continent-wide land cover mapping (DEA Land Cover) of the Australian continent. DEA Land Cover provides annual maps from 1988 to 2020 at 25 m resolution. Output maps were validated with ∼12,000 independent validation points, giving an overall map accuracy of 80%. DEA Land Cover provides Australia with a nationally consistent picture of land cover, with an open-source software package using readily available global coverage data and demonstrates a pathway of adoption for national implementations across the world. [ABSTRACT FROM AUTHOR]

As one of the main components of Grassland Fire Danger Index, grassland curing degree provides crucial information for determining grassland fire danger. Accurate estimates of grassland curing are critical for determining grassland fire risk. This research focuses on the use of Landsat 8 to estimate grassland curing. Results demonstrate that Landsat 8 observations can be used to estimate curing percentages as assessed by visual and ground sampling measurements. Grassland interannual variability for the Greater Melbourne region using Landsat 8 imagery from 2013 to 2019 is examined. Slight differences in curing times and degree are observed for sample sites surrounding Greater Melbourne due to climatic differences across the region. Precipitation is regarded as an essential variable affecting curing degree and this relationship is evident for all five sample sites. Landsat 8 curing results are compared to both visual observations and destructive sampling, the most accurate method, for accuracy assessment. At 95% confidence level, Landsat 8 estimations are no different from destructive ground sampling estimations. Overall, this study validates the use of Landsat 8 data as an effective and accurate way for grassland curing monitoring. [ABSTRACT FROM AUTHOR]

A strategy for the development of the Australian spatial information industry called 'Spatially Enabling Australia' has recently been developed by the Cooperative Research Centre for Spatial Information. It comprises three fundamental research programs and an integrated applications program. Research Program 1, 'Positioning,' underpins a full framework of continuous operating reference stations to ultimately enable all of continental Australia to be capable of real-time precise positioning services based on global navigation satellite systems. Research Program 2, 'Automated Spatial Information Generation,' addresses complex processing of multiple remote sensing sources. Research Program 3, 'Spatial Infrastructures,' helps form the foundation for development of an Australian Spatial Marketplace that will make accessible vast amounts of government held data under a new licensing and access regime which supports combination with user-generated content from the mass market. The three core programs are integrated with Program 4, 'Applications,' to support users from the Health, Defense and Security, Energy and Utilities, Urban Development, and Agriculture-Natural Resources-Climate Change sectors. Program 4 drives outputs from the three core research programs in sector-specific deployments for high impact. This will see a rapid acceleration of the use and value adding of information products and services that utilize spatial information. There are considerable research and development challenges that must be met in order to achieve the strategic outcomes. [ABSTRACT FROM AUTHOR]