Natural hazards, including bushfires and floods, are a constant threat to world’s ecosystem. The occurrence of fire events, for example, depends on multiple factors such as the type of fuel present in the vegetation, temperature of land etc.

Data gathered by different satellite systems can be a key in predicting future fire events and damage assessment. With the availability of large amounts of data, Artificial Intelligence (AI) systems can be developed to help minimize the damage caused by these natural hazards. With data growing exponentially, annotating it is becoming a next big challenge. However, due to the heterogeneity of remote sensing measurements and tasks, there is no single go-to dataset that could serve the purpose of benchmarking. Moreover, annotating this type of data is also a challenge.

To deal with this issue the development of supervised learning algorithm is being shifted towards self supervised learning. These systems operate with high precision and make real time decisions with little to no human intervention. However, the black-box nature of these models gives rise to another set of questions such as which classifier is best for a given condition, what factors are controlling the accuracy of the model. we aim to develop an explainable and interpretable selfsupervised deep learning model that can infuse multi-modal data gathered from satellites and open data sources.

Development of an interpretable decision support system can help organizations take timely actions to prevent the spread of a natural disaster such as forest fires. Our approach would involve training an interpretable vision transformer in a self-supervised manner that could fuse information from multi-modal satellite and geospatial data. For this purpose, Data can be acquired from a range of satellite data available. Datasets of PRISMA, MODIS, Sentinel-2 and Landsat can contribute to a great deal in the development of AI based systems for hazard detection, area mapping and damage assessment.

Australia has a total of 134 million hectares of forest, which is equivalent to 17% of Australia’s land area. Of this total forest area, determined as at 2016, 132 million hectares (98%) are ‘Native forests’, 1.95 million hectares are ‘Commercial plantations’, and 0.47 million hectares are ‘other forest’. Australia has about 3% of the world’s forest area and globally is the country with the seventh-largest forest area. The Australian ecosystem is shaped by fires for over 70 000 years and each ecosystem has its fire regimes. Additionally, fuel management and predicting flammable areas are the key to managing wildfires. These factors play a vital role in resource allocation, mitigation and recovery efforts. Forest fire is a major ecological disaster, which has economic, social and environmental impacts on humans and also causes the loss of biodiversity. Therefore, it is important to know and understand the behaviour of fire ignition and spread so that fire management agencies can prevent and mitigate wildfires. This project aims to develop a tool to predict Forest Fire Spread using Machine Learning approach and Weather Research and Forecasting with fire spread model (WRF-SFIRE).

In this project, fire risk probability mapping, Prediction of fire points, and fire spread modelling will be carried out for the flammable forest areas in Australia. The fire risk probability model will be prepared by using the two-step Analytic Hierarchy Process (AHP) approach. The fire risk probability model will also be used for cross-validation of Support Vector Machine (SVM) model outputs. Then the Prediction of fire points will be done using SVM model (Linear kernel, Polynomial kernel, Radial kernel, sigmoid kernel) taking elevation, slope, aspect, Soil Moisture Layer (SML), Land Surface Temperature (LST), vegetation type layers for training the data set. Then the Weather Research and Forecasting (WRF) model will be used for obtaining meteorological data for the input of fire spread model using the Global Forecast System (GFS) as source data. After that, the fire spread path will be traced for various recent past fire event. The additional variables will be used for spread modelling other than SVM are, canopy cover, wind, temperature. The Cellular Automata (CA) fire spread model will setup in any two different locations of Australia.

This Pilot project aims to demonstrate the AquaWatch system concept for Australian inland and estuarine water bodies. This Pilot project is one of the ‘testbeds’ for new digital monitoring approaches to integrate and visualise ground-based sensor and multiple satellites data streams, combined with modelling application to provide spatially resolved 24/7 information on inland water quality conditions and provide short-term forecasting capability.

In this pilot we will integrate tools and processes for analysing, modelling and forecasting toxic algal bloom. It will highlight the scientific basis for inland water quality monitoring in a multi-sensor space-based system, including crosscalibration process and thereby validating the use of AquaWatch within the Murray-Darling Basin (MDB).

The output will be a ‘a prototype ground-to-space water quality monitoring and forecasting tool’ which is modular and scalable and will inform future operational monitoring and forecasting of toxic algal blooms in the whole of Australian freshwater bodies. This will include a calibrated EO sensor (on-ground and satellite) based BGA blooms detection and monitoring, a prototype computer-based model of blooms forecasting and spatial distribution/maps (dashboard) of waterbodies for a short-term bloom likelihood along with an alert system. Background data on water profile temperature, local meteorological parameters, Chlorophyll-a concentration and cyanobacteria cell counts will be used to develop the prototype early detection and forecasting tools for cyanobacteria biovolume (cell counts) used as triggers for alert levels. This forms an integral part of the AquaWatch mission demonstrating values of an EO based, integrated digital forecasting system to end-user groups for their benefit and adoption. The chlorophyll-a/cell count based results are expected to be used by a wider end-user groups eg., utilities, catchment water managers, river operators, River/Lake tourism and recreation industry, Irrigation industry, aquaculture and mining industries to better manage water bodies. For example, the environmental cost of toxic algal blooms in the Murray River was estimated at $240M/bloom (2010 Ernst & Young Australia report). Results from this project will directly inform operational policy to minimise toxic algal megaevents in future in the Australian inland waters.

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.