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(2022) Zhang, QiThesisAs a dominant terrestrial ecosystem of the Earth, forest environments play profound roles in ecology, biodiversity, resource utilization, and management, which highlights the significance of forest characterization and monitoring. Some forest parameters can help track climate change and quantify the global carbon cycle and therefore attract growing attention from various research communities. Compared with traditional in-situ methods with expensive and time-consuming field works involved, airborne and spaceborne remote sensors collect cost-efficient and consistent observations at global or regional scales and have been proven to be an effective way for forest monitoring. With the looming paradigm shift toward data-intensive science and the development of remote sensors, remote sensing data with higher resolution and diversity have been the mainstream in data analysis and processing. However, significant heterogeneities in the multi-source remote sensing data largely restrain its forest applications urging the research community to come up with effective synergistic strategies. The work presented in this thesis contributes to the field by exploring the potential of the Synthetic Aperture Radar (SAR), SAR Polarimetry (PolSAR), SAR Interferometry (InSAR), Polarimetric SAR Interferometry (PolInSAR), Light Detection and Ranging (LiDAR), and multispectral remote sensing in forest characterization and monitoring from three main aspects including forest height estimation, active fire detection, and burned area mapping. First, the forest height inversion is demonstrated using airborne L-band dual-baseline repeat-pass PolInSAR data based on modified versions of the Random Motion over Ground (RMoG) model, where the scattering attenuation and wind-derived random motion are described in conditions of homogeneous and heterogeneous volume layer, respectively. A boreal and a tropical forest test site are involved in the experiment to explore the flexibility of different models over different forest types and based on that, a leveraging strategy is proposed to boost the accuracy of forest height estimation. The accuracy of the model-based forest height inversion is limited by the discrepancy between the theoretical models and actual scenarios and exhibits a strong dependency on the system and scenario parameters. Hence, high vertical accuracy LiDAR samples are employed to assist the PolInSAR-based forest height estimation. This multi-source forest height estimation is reformulated as a pan-sharpening task aiming to generate forest heights with high spatial resolution and vertical accuracy based on the synergy of the sparse LiDAR-derived heights and the information embedded in the PolInSAR data. This process is realized by a specifically designed generative adversarial network (GAN) allowing high accuracy forest height estimation less limited by theoretical models and system parameters. Related experiments are carried out over a boreal and a tropical forest to validate the flexibility of the method. An automated active fire detection framework is proposed for the medium resolution multispectral remote sensing data. The basic part of this framework is a deep-learning-based semantic segmentation model specifically designed for active fire detection. A dataset is constructed with open-access Sentinel-2 imagery for the training and testing of the deep-learning model. The developed framework allows an automated Sentinel-2 data download, processing, and generation of the active fire detection results through time and location information provided by the user. Related performance is evaluated in terms of detection accuracy and processing efficiency. The last part of this thesis explored whether the coarse burned area products can be further improved through the synergy of multispectral, SAR, and InSAR features with higher spatial resolutions. A Siamese Self-Attention (SSA) classification is proposed for the multi-sensor burned area mapping and a multi-source dataset is constructed at the object level for the training and testing. Results are analyzed by different test sites, feature sources, and classification methods to assess the improvements achieved by the proposed method. All developed methods are validated with extensive processing of multi-source data acquired by Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR), Land, Vegetation, and Ice Sensor (LVIS), PolSARproSim+, Sentinel-1, and Sentinel-2. I hope these studies constitute a substantial contribution to the forest applications of multi-source remote sensing.
Advancing understanding of development policy impacts on transboundary river basins: Integrated watershed modelling of the Lower Mekong Basin.(2021) Ly, KongmengThesisThe management of transboundary river basins across developing countries, such as the Lower Mekong River Basin (LMB), is frequently challenging given the development and conservation divergences of the basin countries. Driven by needs to sustain economic performance and reduce poverty, the LMB countries are embarking on significant land use changes in the form hydropower dams, to fulfill their energy requirements. This pathway could lead to irreversible changes to the ecosystem of the Mekong River, if not properly managed. This thesis aims to explore the potential effects of changes in land use —with a focus on current and projected hydropower operations— on the Lower Mekong River network streamflow and instream water quality. To achieve this aim, this thesis first examined the relationships between the basin land use/land cover attributes, and streamflow and instream water quality dynamics of the Mekong River, using total suspended solids and nitrate as proxies for water quality. Findings from this allowed framing challenges of integrated water management of transboundary river basins. These were used as criteria for selecting eWater’s Source modelling framework as a management tool that can support decision-making in the socio-ecological context of the LMB. Against a combination of predictive performance metrics and hydrologic signatures, the model’s application in the LMB was found to robustly simulate streamflow, TSS and nitrate time series. The model was then used for analysing four plausible future hydropower development scenarios, under extreme climate conditions and operational alternatives. This revealed that hydropower operations on either tributary or mainstream could result in annual and wet season flow reduction while increasing dry season flows compared to a baseline scenario. Conversely, hydropower operation on both tributary and mainstream could result in dry season flow reduction. Both instream TSS and nitrate loads were predicted to reduce under all three scenarios compared to the baseline. These effects were found to magnify under extreme climate conditions, but were less severe under improved operational alternatives. In the LMB where hydropower development is inevitable, findings from this thesis provide an enhanced understanding on the importance of operational alternatives as an effective transboundary cooperation and management pathway for balancing electricity generation and protection of riverine ecology, water and food security, and people livelihoods.
Accelerating Australia’s electric vehicle uptake: Overcoming socio-technical inertia and bridging the gaps with public policy options designed to transform road transport for a decarbonised future(2023) Broadbent, GailThesisTo obviate significant and growing road vehicle greenhouse gas (GHG) emissions contributing to climate change, transitioning to battery electric vehicles (BEV) is urgently required to maximise fleet emissions reductions soonest, deploying the most suitable available technology. Many countries have implemented policies to incentivise electric vehicle (EV) uptake, which have been well studied. This thesis undertakes novel research by employing a case study of New Zealand to examine consumer responses to EV policies implemented in 2016, plus two mooted policies. Questionnaires and interviews surveyed private motorists from a demand perspective, capturing quantitative and qualitative data to assess attitudes, values, and perceptions of EVs, awareness of government policies, and to reveal those most popular. Employing a unique innovation, four motorist groups (segmented by attitude to EVs, which influences adoption rates) were compared. As additional novelty the role of communication channels, including print media, in influencing consumer behaviour was investigated. Results revealed New Zealand’s conventional motorists, in contrast with EV owners, had low policy awareness, confirming international findings. EV Positives, the next-most ‘EV ready’ segment, favoured policies designed to reduce EV purchase price and increase nationwide charger deployment. Concordant with social marketing research, governments should focus on such buyers’ preferences. Furthermore, to improve BEV readiness, disseminating updated information about EVs via multiple communication channels could shift perceptions of EVs from ‘expensive and inconvenient’ to ‘fun and economical’. Thus, two key concepts namely purchase price-parity and charging infrastructure availability, were incorporated into models specifically for Australia, where policies are limited, to investigate the feasibility of transitioning Australia’s road vehicle fleet to electromobility to achieve net-zero emissions by 2050. A national scale, integrated, macro-economic, system dynamics model (iSDG Australia) was used innovatively to project Australia’s future road transport demand, vehicle mix, energy consumption and GHG emissions. Firstly, the model applied numerous ‘adoption target’ scenarios comparing them to Business-as-Usual; secondly, various combinations of policy options were modelled to project potential outcomes and implementation costs. Based on the assumptions, results suggest emissions reductions are maximised by the fastest passenger vehicle fleet transition to BEVs, entailing declining but ongoing transformational government policy support to achieve net-zero by 2050.
Wastewater-based monitoring and genomic characterisation of antibiotic-resistant bacteria in the Sydney community(2023) Zillur Rahman, Kazi MohammadThesisCurrent healthcare infection surveillance rarely monitors the distribution of antimicrobial resistance (AMR) in bacteria beyond clinical settings in Australia and overseas. This results in a significant gap in our ability to fully understand and manage the spread of AMR in the general community. This thesis explores whether wastewater-based monitoring could reveal geospatial-temporal and demographic trends of antibiotic-resistant bacteria in the urban area of Greater Sydney, Australia. Untreated wastewater from 25 wastewater treatment plants sampled between 2017 and 2019 consistently contained extended-spectrum β-lactamases-producing Enterobacteriaceae (ESBL-E) isolates, suggesting its endemicity in the community. Carbapenem-resistant Enterobacteriaceae (CRE), vancomycin-resistant enterococci (VRE), and methicillin-resistant Staphylococcus aureus (MRSA) isolates were occasionally detected. Demographic and healthcare infection-related factors correlated with the ESBL-E load, and demographic variables influenced the VRE load. In contrast, the healthcare infection-related factor mainly drove the CRE load. These findings demonstrate the potential of wastewater-based surveillance to understand the factors driving AMR distribution in the community. The subsequent thesis work covers the genomic characterisation of selected ESBL-E and CRE wastewater isolates to reveal their nature, origin, and underlying resistance mechanisms. Phylogenetic analysis showed that Escherichia coli isolates were related to high-risk human-associated pandemic clones and non-human-associated clones. The Klebsiella pneumoniae and K. variicola isolates were related to globally disseminated and emerging human-associated clones, and some were detected for the first time in Australia. Genomic analysis also indicated novel resistance mechanisms against nitrofurantoin in E. coli, and against piperacillin/tazobactam and ticarcillin/clavulanic acid in Klebsiella isolates. The virulence gene content indicated that some E. coli and Klebsiella isolates were likely associated with infections, while the asymptomatic carriage was suggested for other isolates. These results demonstrate a clear potential for wastewater-based surveillance to monitor the emergence and dissemination of resistance in non-clinical isolates, and in particular, isolates from the community and non-human sources. The findings of this study can complement healthcare infection surveillance to inform management strategies to mitigate the emergence and dissemination of AMR and important human pathogens in the general community.
(2023) Wang, JieThesisSugar is Australia's second largest export crop after wheat, generating a total annual revenue of almost $2 billion. It is produced from sugarcane, with approximately 95% grown in Queensland. While highly productive and contributing to the area’s economic sustainability, the soils in these areas have low fertility. The soils typically contain sand content > 60%, low organic carbon (SOC < 0.80%), cation exchange capacity (CEC), exchangeable Ca and Mg (< 8, 2.0, and 0.25 cmol(+) kg-1, respectively). Moreover, the soil is acidic (pH water < 5.5) and sodic (exchangeable sodium percentage [ESP] > 6%). Hence, sugarcane farmers need to apply fertilisers and ameliorants to maintain soil quality and productivity. Unfortunately, the high intensity rainfall in the region results in sediments, nutrients, and ameliorants run-off from these farms, resulting in environmental degradation and threats to marine ecology in the adjacent World Heritage Listed Great Barrier Reef. To mitigate these issues, the Australian sugarcane industry introduced the Six-Easy-Step Nutrient Management Guidelines. To apply these guidelines, a labour-intensive high-density soil sampling is typically required at the field level, followed by expensive laboratory analysis, spanning the myriad of biological, physical, and chemical properties of soils that need to be determined. To assist in sampling site selection, remote (e.g., Landsat-8, Sentinel-2, and DEM-based terrain attributes) and/or proximal sensing (e.g., electromagnetic [EM] induction and gamma-ray [γ-ray] spectrometry) digital data are increasingly being used. Moreover, the soil and digital data can be modelled using geostatistical (e.g., ordinary kriging [OK]), linear (e.g., linear mixed model [LMM]), machine learning (e.g., random forest [RF], quantile regression forest [QRF], support vector machine [SVM], and Cubist) and hybrid (e.g., RFRK, SVMRK, and CubistRK) approaches to enable prediction of soil properties from the rich source of digital data. However, there are many questions that need to be answered to determine appropriate recommendations including but not limited to i) which modelling approach is optimal, ii) which source of digital data is optimal and does fusion of various sources of digital data improve prediction accuracy, iii) which methods can be used to combine these digital data, iv) what is a minimum number of samples to establish a suitable calibration, v) which soil sampling designs could be used, and vi) what approaches are available to enable prediction of soil properties at various depths simultaneously? In this thesis, Chapter 1 introduces the research questions and defines the problems facing the Australian Sugarcane Industry in terms of the applications of the Six-Easy-Steps Nutrient Management Guidelines, research aims and thesis structure. Chapter 2 is a systematic literature review on various facets of DSM, which includes digital and soil data, models and outputs, and their application across various spatial scales and properties. In Chapter 3, prediction of topsoil (0-0.3 m) SOC is examined in the context of comparing predictive models (i.e., geostatistical, linear, machine learning [ML], and hybrid) using various digital data (i.e., remote [Landsat-8] and proximal sensors [EM and γ-ray]) either individually or in combination and determining minimum number of calibration samples. Chapter 4 shows to predict top- (0-0.3 m) and subsoil (0.6-0.9 m) Ca and Mg, various sampling designs (simple random [SRS], spatial coverage [SCS], feature space coverage [FSCS], and conditioned Latin hypercube sampling [cLHS]) were assessed, with different modelling approaches (i.e., OK, LMM, QRF, SVM, and CubistRK) and calibration sample size effect evaluated, using a combination of proximal data (EM and γ-ray) and terrain (e.g., elevation, slope, and aspect, etc.) attributes. Chapter 5 shows to enable the three-dimensional mapping of CEC and pH at topsoil (0-0.3 m), subsurface (0.3-0.6 m), shallow- (0.6-0.9 m) and deep-subsoil (0.9-1.2 m), an equal-area spline depth function can be used, with remote (Sentinel-2) and proximal data (EM and γ-ray) used alone or fused together, and various fusion methods (i.e., concatenation, simple averaging [SA], Bates-Granger averaging [BGA], Granger-Ramanathan averaging [GRA], and bias-corrected eigenvector averaging [BC-EA]) investigated. Chapter 6 explored the synergistic use of proximal (EM and γ-ray), and time-series of remote data (Landsat-8 and Sentinel-2) to map top- (0-0.15 m) and subsoil (0.30-0.45 m) ESP. The results show that, across these case studies, hybrid and ML models generally achieved higher prediction accuracy. The fusion of remote and proximal data produced better predictions, compared to single source of sensors. Granger-Ramanathan averaging (GRA) and concatenation were the most effective methods to combine digital data. A minimum of less than 1 sample ha-1 would be required to calibrate a good predictive model. There were differences in prediction accuracy amongst the sampling designs. The application of depth function splines enables the simultaneous mapping of soil properties from various depths. The produced DSM of soil properties can be used to inform farmers of spatial variability of soils and enable them to precisely apply fertilisers and/or ameliorants based on the Six-Easy-Step Nutrient Management Guidelines.