Active GIS/RS research projects
Multimodal data acquisition and feature extraction from multi-sensor terrestrial mobile mapping systems Geoff West
This is project 2.1 of the CRC for Spatial Information. It will research and develop techniques and algorithms for the detection, recognition and analysis of low-level and high-level features from mobile mapped data along urban and rural transport corridors. Mobile mapped data is acquired from near ground vehicle sensors that gather information not available from aerial and satellite platforms. Low-level features include edges, corners, planar surfaces, and high-level features include buildings, road signs and the transport corridor reserve. The research will use fused image video, laser, lidar and other available data acquired from mobile platforms to recognise, locate and measure the various features of interest. Discussions with various end-users have shown that automatic feature extraction is essential for organisations such as local government authorities and other government agencies, surveying companies and members of 43 P/L, to measure features, more easily manage transport corridor and associated assets as well as assess the integrity of important infrastructure such as power lines, transport surfaces and street side furniture.
This is project 4.4.1 of the CRC for Spatial Information. It aims to develop tools to disseminate and translate spatio-temporal health information and analysis results to end users in the health sector, allowing them to:
- discover gaps in health service delivery and identify populations of greatest health risk, and
- communicate these identified gaps/risks to program leaders, decision makers and health researchers for making informed and evidence-based decisions.
It will allow end-users to identify:
- where the areas of greatest risk are,
- what the potential economic and social impacts are,
- what and where the health services are most needed,
- how alternative health services can be quantified, compared and prioritised, and
- if we are spending our budgets effectively and efficiently.
It will also identify and enhance the best available spatio-temporal modelling software that should be made available to end-users in health and invest resources into new methods to protect patient privacy.
Pastoral lease assessment using geospatial analysis (PLAGA) - Remote sensing based rangeland condition assessment Todd Robinson
Past and present land management practices have, in some areas, led to a decline in the condition of Western Australia’s rangelands. The condition of our rangelands therefore needs to be monitored to ensure their sustainability into the future. The PLAGA project was initiated to add efficacy to existing ground-based sampling that has previously been undertaken along dirt tracks. To provide an adequate representation of the condition and trend of a lease, sampling needs to be repeated and designed to capture all variability within a lease. Unfortunately, for reasons of cost and accessibility these two conditions are rarely met from ground-based surveys and thus seriously degraded areas that have not been assessed in situ may go unnoticed. To date the PLAGA project has:
- Developed methods for the selection of a suitable vegetation index from a wide range of potential candidates;
- Finely tuned the selection of a benchmark (or threshold) to dichotomise between poor and good condition classes;
- Utilised retrospective sequences of Landsat imagery for examining historical management; and
- Provided user outputs in the form of state and transition models and long-term trend summaries to not only identify areas that are in poor condition but also to highlight areas that are trending towards poor condition.
These outputs have can be effective tools for triggering land management responses such as lowering stocking numbers, destocking or other remediation measures to avoid long-term damage to our rangelands. In addition, these tools also show areas that have recovered or are recovering from previous disturbances and thus can be used to gauge the success of intervention strategies and/or natural recovery.
Analysis and modelling of relationships between the dynamics of the mosquito-borne Murray Valley encephalitis virus (MVEV) and spatio-temporal environmental variability Grit Schuster
A surveillance programme of the Western Australian (WA) Department of Health carrie Mosquito-borne Murray Valley encephalitis virus (MVEV) is a virus that is permanently present in North Western Australia (WA) but occasionally occurs in other parts of Australia. It can cause deadly encephalitis in humans although the fatality rate of 25% is relatively low. However, 25 to 50% of the infected people are permanently affected due to neurological damage which is associated with high cost of care.
A surveillance programme of the Western Australian (WA) Department of Health carried out by the UWA is currently in place. Within the programme sentinel chickens are used throughout WA to monitor virus activity in the main populated locations. Unfortunately, this surveillance is especially spatially strongly restricted to the sentinel chicken test sites and little is known about virus activity in between those locations.