Tracking landscape function with the capacity to identify degradation versus regeneration is fundamental to sustainable land management. Spatial changes in soil biophysical state could be monitored by identifying the parameters that contribute to resilience and functional integrity. Soil health is the foundation of landscape function and is paramount to the sustainable management of savannas and grasslands across Northern Australia that occupy approximately two million km2 (Williams et al., 2005). Climatic extremes are increasingly changing these landscapes which require adaptation in land management strategies to deal with more frequent droughts, floods, and fires. Hence, there is an incentive for accurate and rapid monitoring to quantify spatial changes from soil to pastures and higher vegetation.

This research will apply a range of sensing platforms to characterise ground cover dynamics, analysing signal variations before and after fire, drought, floods and the impacts of grazing. To enhance capabilities and provide detailed analysis, Unmanned Aerial Vehicle (UAV) and a hand-held field spectrophotometer will be used to study the landscape function and soil health. Potentially, the phototrophic fabric called biocrusts that inhabit the soil surfaces in the northern savannas could act as a soil health indicator, by means of its biodiversity and its microbial communities’ capacity to act as ecosystem engineers (Eldridge & Leys, 2003). Both UAV and handheld devices are adept to quantifying chlorophyll in biocrusts nondestructively by detecting specific wavelength bands across the entire electromagnetic hyperspectral range (Gitelson, Gritz, & Merzlyak, 2003). Such spectrophotometry would enable a clearer understanding of the nuances of ‘bare ground’ by mapping the distribution of chlorophyll at a field level and across landscapes both temporally and spatially to recognize the role of biocrusts as a surrogate measure for soil health.

Various studies have shown the utilisation of hyperspectral measurements accurately determine the chlorophyll concentration of plants and biocrusts at several scales (Román et al., 2019; Stephens, Louchard, Reid, & Maffione, 2003).
Furthermore, digital colour (RGB, red, green blue) image analysis is another non-destructive method that can accurately measure chlorophyll content (greenness), a plant health Third Party Project Agreement | SmartSat CRC | P3-32s Page 4
indicator, and nutrient levels. The advent of fixed camera and high-performance computing, over the past decade, has facilitated the use of high-resolution visible photos (Red, Green, Blue) and digital technologies (including machine learning) more readily.

In this research, I will work towards linking proximal and remote sensing to provide soil health management tools. The project will be based on 30-years of fire research at Victoria River Research Station (VRRS), Northern Territory, and the impacts of grazing management regimes (established 24 years) at Wambiana Station (WGT) in North Queensland. This research will enable the connection of soil health to management practices. In a progressively variable climate monitoring ecosystem interactions is critical to guiding land management. Furthermore, sensing technologies have the capacity to enhance insight and decision making to maintain resilience and ongoing landscape function.

This project responds to a request from Defence Science and Technology Group (DSTG) to develop a visualisation of SmartSat CRC research activities to provide context for Defence capability managers. This initial activity will draw on conceptual work to show how Indo-Pacific Connector will deliver maritime domain awareness through space-based sensors and advanced communication technologies.

This project will research a novel deep learning pipeline for achieving robust and reliable forecasting of natural disaster events with hyperspectral satellite imagery. The majority of the existing machine learning algorithms do not possess the ability to forecast the occurrence of natural disasters in advance and they are only able to detect their occurrence when the disaster event happens. To address this limitation, we propose novel research which leverages the spatiotemporal modelling capability of deep learning to forecast the occurrence of natural disasters in advance using a satellite onboard execution environment. The research will be conducted using the example use cases from the application area of bushfire event forecasting considering the practical significance to Australia. The developed framework will be deployable on a CubeSat Kanyini-type mission with Hyperscout-2 payload and will use onboard hardware to execute the algorithm.

This pilot project aims to demonstrate success of the AquaWatch system concept on a pilot scale for Australian coastal water bodies. Through development of new partnerships with regional water managers in two locations, the pilot study provides ‘testbeds’ for new approaches 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).

The result will be a demonstrator ground-to-space water quality monitoring tool with a prototype front-end visualisation tool (web/mobile-based dashboard) to display spatial maps of water quality parameters, time series of hydrodynamic, biogeochemical and optical parameters and statistical analysis to aid in decision making. In the longer-term, outcomes from the project can be in the form of new sensor technologies, new software platforms and/or packaged products. 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.

This project aims to demonstrate the potential of machine learning (ML)-based algorithms and models in decoupling the complex optical signature in coastal waters and deriving water quality parameters in coastal waters from satellite observations. Through collaborating with CSIRO researchers and our industry partners, this project will investigate the potential of new machine learning approaches in predicting water quality parameters through fusing data from insitu water quality sensors and satellite observational data. We will develop a machine learning model to invert the remote sensing reflectance signal and to derive WQ parameters.

The result will be a machine learning-based modelling tool to improve the water quality products and thus enable better coastal ecosystem management. New ML-based remote sensing products from this project will help end users in the Cockburn Sound and Moreton Bay regions improve their coastal monitoring and management practices.

Blue Carbon, carbon that is stored in aquatic ecosystems, has been identified as an essential component of the global carbon cycle with the power to reduce inequities amongst nations through valuation of carbon sequestration in a global Blue Carbon economy. Foundational works discussing Blue Carbon include only three component ecosystems of Blue Carbon, namely mangroves, seagrasses, and salt marshes. Kelp is however capable of very high rates of primary production, with past work recording kelp fixing above 3000 g of carbon per square meter per year. Recent work by Filbee-Dexter and Wernberg (2020) estimated the carbon content of kelp forests in the Australian Great Southern Reef alone to be roughly equivalent to 3% of global Blue Carbon. Kelp forests have additionally been shown to sequester much of the carbon they fix through debris exports to deeper waters.

Comprehensive and accurate Blue Carbon estimates must therefore include kelp forests. Further study is however needed regarding how to include kelp forests into Blue Carbon budgets. Space-based remote sensing, for example multispectral imagery publicly available through the Landsat and Sentinel programs, provides the high quality, periodic data needed to effectively study kelp at a large scale. Considering that past research into Blue Carbon has had an outsized focus on the Global North (e.g., seagrasses, though distributed throughout both hemispheres and all inhabited continents, have been most heavily studied in the Mediterranean and North Atlantic), relying on open data sources that provide planetary coverage, such as those mentioned above, to develop a kelp detection workflow would avoid reinforcing pre-existing geographic biases. If successful, using satellite imagery to map and monitor kelp forests could thus create a global, standardized inventory methodology upon which conservation policies and a
robust Blue Carbon economy could be based.

This proposed doctoral research aims to supply the scientific knowledge required to develop and assess a system using earth observation data to map and monitor kelp forests and their biophysical properties, specifically in Australian waters, to enable their inclusion in Carbon Accounting Systems and a Blue Carbon economy. There are four proposed objectives:

1. Evaluate the utility of space-based Earth Observation (EO) in monitoring kelp forests at the regional to national scale, considering ecosystem characteristics that
   may affect monitoring success (eg canopy density, water depth, species composition);
2. Develop an Earth Observation system for mapping and monitoring kelp forest extent and biomass in Australian waters;
3. Quantify the sources and values of uncertainties present throughout kelp extent and biomass mapping processes in the project; and
4. Produce a national-scale kelp inventory using publicly available satellite imagery and estimate Australia’s kelp carbon stock and sequestration values with their associated uncertainties.

This work will thus produce not only scientific knowledge and a reproducible methodology, but also a data product that can be used in management and policy decisions, addressing a significant Australian and global knowledge gap.