Research Programs

Knowledge gaps and opportunities for earth observation tools in mine-rehabilitation at the property scale

EO Analytics

Australian industries and land managers are required to restore and rehabilitate land that they have disturbed. However, these industries and government regulators lack Earth Observation (EO) tools to effectively characterise progressive rehabilitation of these disturbance footprints.

This project aims to engage the mining industry and land management agencies to develop a clear understanding of EO tools needed to improve government monitoring, and industry management approaches to mine rehabilitation and restoration at the property scale (including stochastic event monitoring). We know from recent discussions with multiple mining companies (e.g. BHP, Anglo, and Rio Tinto) that there is a compelling argument to link remotely sensed observations with rehabilitation success criteria.

This project includes a needs analysis based on industry and government engagement; a literature review on EO for rehabilitation monitoring and tool development; and the development of a roadmap that highlights current capabilities and recommendations for future research.

P3.09

Project Leader:
Associate Professor Peter Erskine, University of Queensland

Participants:

Enhancing Earth Observation for Maritime Domain Awareness (E04MDA) – Phase 1

EO Analytics

EO4MDA is ultimately planned to be a multi-phased project aimed at enhancing Earth Observation practices and to generate robust and reliable information about the maritime domain to support the Commonwealth interdict and enforce laws across its Exclusive Economic Zone and coastal areas.

EO4MDA will produce a downstream processing capability to correlate, federate and reason from data sourced through ground, air, and naval-based systems integrated with space-based Synthetic Aperture RADAR (SAR) data reports generated by the COSMO-SkyMed constellation data, satellite-based optical data as well as satellite-based AIS and SIGINT collections.

This project, EO4MDA Phase 1, is the first collaborative step to test the limits and bounds of new AI/ML/statistics-based processing methods in a realistic scenario employing constrained space-based Earth Observation data. The aim is to resolve operational queries (anomalous vessels and vessel behaviour) put to system in a real world demonstration with maritime decision-makers with space-based Earth Observation.

In particular, it is currently best practice to exploiting non-cooperative characteristics of vessels from space imaging sensors for the detection of anomalies:

  • vessels with failed or malfunctioning GPS and/or transmitting equipment;
  • vessels that deliberately “turn off” the System so as to avoid detection;
  • vessels that, because of their smaller size, are not under the obligation of having an on board positioning system as well as sport fishing vessels;
  • abnormal behaviours

P3.10

Project Leader:
George Coulloupas, Leonardo Australia

Participants:

Earth Observation Analytics Solutions: Know the Market to Grow the Market

EO Analytics

This 15-month project seeks to  develop a commercial assessment of a focused selection of Earth Observation (EO) end user needs to define future SmartSat CRC Research activities and projects.

The research will address what end user problems EO can address and which of these problems people will pay to have solved. The output will be a report covering defined problems, customers, commercial viability, due diligence and top level mapping to SmartSat partner capabilities that can inform research that can be progressed now or requires capability development to progress.

P3.03

Project Leader:
Phil Delaney, FrontierSI

Participants:

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

Smart Mission Design

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

EO Analytics

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

EO Analytics

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: