An AquaWatch Pilot which is aimed at supporting the Aquaculture Industry in the Spencer Gulf, and the associated need for monitoring environmental impact. This pilot project aims to demonstrate the integration of in-situ sensor data streams and satellite water quality products.

Project activity: This pilot project, working closely with state government agencies and aqua-culturalists, will demonstrate the potential benefits of data derived from in-situ sensors (Hydraspectra, weather station) and earth observation satellites (sentinel 2, LandSat-8) to integrate and visualise multiple space-based observations (maps of sediment, dissolved carbon, chlorophyll-a and diffuse attenuation of light) and ground-based sensor water quality data (temperature, salinity, fluorescence, backscattering, turbidity and hyperspectral reflectance) streams, combined with hydrodynamic model outputs (salinity, temperature, stratification and current vectors).

Expected outputs and outcomes: Project result will be a demonstrator data processing, integration and visualisation tool to support environmental monitoring and sustainable growth of the aquaculture industry. The ultimate aim is the demonstrated value of the integrated technologies, their benefit to end-users and their potential adoption. These are required to inform the development of the AquaWatch mission. Results will be available to inform the relevant industry sectors for commercial development to the benefit of the Australian economy.

The aim of the study is to review how existing and future EO data can be used in combination with data capture with terrestrial methods in a joint data fusion architecture. To create the architecture, standards of the terrestrial and satellite data will be investigated and when required, recommendations for standards’ improvement will be suggested. The proposed architecture will be designed as general, applicable in multiple spatial information areas, the study will focus especially on Urban Heat Islands (UHIs). Approaches to efficient data and information visualisation will be part of the investigation in this research with professionals from different areas than spatial information science as target audience. This contributes to the overall SmartSat CRC topic of Environmental Management. Urban Heat Islands impact the quality of life in many urban centres. Metropolitan areas of Australian cities and urbanised regional centres, in particular, show vulnerability towards UHIs due to challenging climatic conditions.

Knowledge of crop and pasture quality can provide the industry with insights to assist with the grazing management of pastures and input management decisions for crops. Handheld and lab-based spectroscopy have been extensively employed to monitor quality-based plant attributes. The methods employed are time consuming and expensive to implement and do not provide the industry with insights into the temporal trends of the critical variables. High resolution and frequent return time can overcome numerous deficiencies affecting equivalent visible IR and SWIR platforms, that limit the ability to create a viable product around crop and pasture quality. This project would conduct a feasibility analysis that capitalises on existing and planned satellite missions, including the Aquawatch satellites and precursors to test development of new high frequency products for crop and pasture quality across the Australian landscape.

Machine learning has achieved great success in computer vision, natural language processing and other fields, especially in the accuracy of prediction and some may have exceeded the human capabilities. Nevertheless, users still need to understand the reasons for their conclusions in a more detailed and tangible way in application scenarios. Giving strong explainability to the model also helps to ensure its robustness, and usability of the method. This proposal focuses on developing interpretable models for early smoke wild-fire detection from satellite images.

Detecting fires at their early stages is essential to prevent fire caused disasters. Research has been conducted to detect smoke in satellite imagery for fire detection. Unfortunately, the imagery data used in previous research have low spatial resolution and only contain the RGB bands, which are ineffective for early fire detection. Our team (Data Analytics Group at UniSA) has been working on early fire smoke detection with multispectral multi-sensor satellite imagery for one year and the accuracy of wild fire detection can reach more than 90% accuracy. An AI framework of deep learning neural networks that identifies wild fires has been developed. It is necessary to present users with understandable reasons for the detection so the users can validate the detection and assess its severity. Current detection models are deep learning-based and have a black box detection kernel. This project aims to make the detection transparent to users so users can use and interact with the model easily. In the following, we will introduce the main techniques for explaining predictions made by deep learning / black-box
machine learning models.

The objective of this project is to assess the suitability of the Canadian WildFireSat mission for Australian Bushfire Management.

The key innovation of this project is the development of robust methods for the integration of radar-based EO data into current and emerging systems for monitoring the impact of fire on vegetation. Rapid fire extent mapping, including fire progression of large wildfires, will be based on dense time-series of synthetic aperture radar (SAR), optical data and machine learning. The research will also explore the capabilities of SAR and LiDAR data, integrated with optical data, for distinguishing structural characteristics of post-fire recovery dynamics.

The overarching focus of the research is on the integration of multi-sensor EO data to fill key gaps in operational monitoring of the impacts of wildfire. The project aims to support land and fire managers to make more informed decisions, by developing more accurate and timely measures of burnt area extent and tools for monitoring post-fire recovery.