SmartSat is integrating the capabilities from the priority areas set in SmartSat’s Technology Roadmap across three primary research program areas as below.

Advance communications and more intelligent satellites will give us the ability to generate higher resolution, higher frequency data.  This will enable us to generate image data from sensors and high resolution real-time video so that we can monitor our land, oceans and our environment in real time.

AI, deep learning and other advanced analytics techniques will enable us to build analytical models and AI systems that can analyse satellite generated data and provide insights or trigger actions in real time.

See projects in Next Generation Earth Observation Data Services projects below:

Topic of this theme may include:

  • Secure & integrated data intensive space systems
  • Customer-centric EO Analytics
  • EO data fusion architectures
  • EO data visualization
  • Hyperspectral sensing
  • Design methodologies for mission specific services
  • Integration Test bed for rapid product development

AquaWatch Pathfinders: Earth Observation Sensor Design Simulator Testbd (End to End Simulator)

Next Generation Earth Observation Data Services

For industry to develop new requirements for sensors, it is imperative that industry users are able to understand and demonstrate the specifications of the proposed sensors in relation to their specific use cases. At the current time this is achieved by utilising parameters that have been used in airborne collections or by estimating the requirements from expectations of the use case. This includes, number of spectral bands, width and positioning of bands, spectrum coverage (eg 380 to 1000 nm for aquatic indices; 400-2400nm for vegetation indices, 1000-3000nm for mineral detection), as well as radiometric and spatial resolution. Having a tool that can simulate these requirements and test the sensor trade-off outcomes, provides a capability that will enhance the efficient and cost-effective design of future sensors, tailored to industry requirements as well as empower the value-adding information industry (VAI) to develop appropriate Earth observation algorithms.

A virtual testbed in the form of an end-to-end simulation tool is an essential component of the design process of any new Earth imaging system (CEOS, 2018). These tools in Australia currently consist of ad-hoc compilations of different advanced software packages and their parameterisations spread over research groups. Optimization of sensors requires understanding the effects of instrumental and environmental parameters on the resulting image characteristics, which can be achieved through precise simulation of sensor images. For this purpose, we propose to develop a SmartSat CRC simulation suite for Earth Observation Sensor Design as well as VAI algorithm development and testing. The software enables simulation of satellite EO sensor measurements over most possible variations of aquatic ecosystems, using a range of sensor design specifications, and subsequent simulation of at-earth-surface image products. There are no commercial implementations of this capability as a software suite. The different stages of the processing chain for SmartSat CRC Earth observation (from high altitude platform or satellite) processing chain are depicted below. They comprise a scene simulator and its inversion process which are briefly described in the following.

P3.13

Project Leader:
Dr Arnold Dekker, SatDek Pty Ltd

Participants:

All-Weather, Near Real-Time Monitoring of Bushfire with Satellite SAR

Next Generation Earth Observation Data Services

Bushfire is a constant threat to Australia which has been exacerbated by climate change. For example, the 2019 20 Australian bushfire season, colloquially known as the Black Summer, was a period of unusually intense bushfires in many parts of Australia, started in June 2019 and continued through to May 2020. In total, more than 9,352 buildings were destroyed with 34 direct deaths and 417 indirect deaths due to bushfire smoke inhalation.

The aim of this project is to exploit interferometric coherence of high resolution satellite SAR imagery in order to detect bushfire early and reliably, monitor its spread day and night and in all weather conditions, and hence greatly improve our bushfire management capabilities. Project activities include not only the development of a robust satellite SAR based ‘production line’ to convert sensing imagery to fire intelligence, but also a number of comprehensive bushfire case studies across states. The expected outcomes are:

1) an innovative tool based on satellite SAR (e.g. 3 metre resolution Sentinel-1A, 1B and NovaSAR) ready to be integrated into the existing bushfire information systems such as the Digital Earth Australia Hotspot (DEAH) system, which complements its planned upgrade through the addition of shortwave infrared information from geostationary satellites (e.g. Himawari-8);

2) technical reports on several case studies of using the new tool to detect and monitor bushfires which may occur in the 2021-2022 and 2022-2023 fire seasons; and 3) a report detailing recommendations for future follow-on project phases by a group of workshop attendees of industry-gathered emergency services clients.

P3.19

Project Leader:
Brittany Dahl, Geoplex Pty Ltd

Participants:

Adaptive Analytical Bushfire Likelihood (AABL) Tool for Better Understanding and Reducing Future Bushfire Risk

Next Generation Earth Observation Data Services

Bushfires are an important aspect of the Australian landscape, but also one of our most destructive and costly natural hazards. As populations grow and expand, more people, assets and infrastructure are exposed to bushfire threats.

The effects of climate change further increase the likelihood and severity of such fires, compounding their overall impact on the community. Consequently, there is an urgent need to identify current areas of bushfire risk and how these are likely to change into the future. Better understanding the spatial and temporal distribution of bushfire likelihood, and its relation to population, infrastructure and ecological assets, will help deliver the most effective risk reduction strategies.

To support the decisions that underpin these strategies, spatially explicit and temporally dynamic information is required to accurately reflect landscape hazard and impact characterisation. Remotely sensed earth observations (EO) can help facilitate provision of this information, as they provide a rich source of synoptic, continuous, and repeatable empirical measures of the landscape.

The core aim of this project is to demonstrate how the use of EO data can assist with identifying how bushfire likelihood can change in space and time, allowing more informed and transparent decision-making for reducing bushfire risk. The project will develop an analytical tool – the Adaptive Analytical Bushfire Likelihood (AABL) Tool – that utilises EO data such as vegetation, soil moisture, meteorological and climatic variables as inputs to a model to map the spatial and temporal distribution of bushfire likelihood.

Working with our end users – the SA and WA government – the tool will deliver a new suite of analytical and modelling capabilities and information dashboards that can integrate with and support these State Governments’ Bushfire planning strategy.

P3.23

Project Leader:
Dr Holger Maier, University of Adelaide

Participants: