ORCID Profile
0000-0002-5421-5838
Current Organisations
RMIT University
,
Czech University of Life Sciences Prague
,
Shahid Beheshti University
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Land use and environmental planning | Urban and regional planning |
Publisher: MDPI AG
Date: 07-01-2022
DOI: 10.3390/RS14020261
Abstract: Satellites offer a way of estimating rainfall away from rain gauges which can be utilised to overcome the limitations imposed by gauge density on traditional rain gauge analyses. In this study, Australian station data along with the Japan Aerospace Exploration Agency’s (JAXA) Global Satellite Mapping of Precipitation (GSMaP) and the Bureau of Meteorology’s (BOM) Australian Gridded Climate Dataset (AGCD) rainfall analysis are combined to develop an improved satellite-gauge rainfall analysis over Australia that uses the strengths of the respective data sources. We investigated a variety of correction and blending methods with the aim of identifying the optimal blended dataset. The correction methods investigated were linear corrections to totals and anomalies, in addition to quantile-to-quantile matching. The blending methods tested used weights based on the error variance to MSWEP (Multi-Source Weighted Ensemble Product), distance to the closest gauge, and the error from a triple collocation analysis to ERA5 and Soil Moisture to Rain. A trade-off between away-from- and at-station performances was found, meaning there was a complementary nature between specific correction and blending methods. The most high-performance dataset was one corrected linearly to totals and subsequently blended to AGCD using an inverse error variance technique. This dataset demonstrated improved accuracy over its previous version, largely rectifying erroneous patches of excessive rainfall. Its modular use of in idual datasets leads to potential applicability in other regions of the world.
Publisher: MDPI AG
Date: 02-02-2023
DOI: 10.3390/SU15032678
Abstract: Identifying and quantifying ecotourism opportunities are critical processes in sustainable tourism planning, which is challenging, since ecotourism is a Complex Adaptive System (CAS). This study investigated Ecotourism Opportunities Measurements (EOMs) in the literature and mapped the research trends to provide practical implications for research in this area. A systematic quantitative literature review began with a scientometric analysis in CiteSpace to examine the existing knowledge and the state of the art in EOMs. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) protocol was then applied to refine the initial search results, and snowballing was used to collect additional articles. The refined set was then thematically coded and quantitatively analyzed. Our findings show that existing studies on ecotourism opportunities predominantly focus on the impacts of ecotourism on the environment, stakeholders’ contributions toward ecotourism development, sustainability, and responsible behavior of local communities in ecotourism promotion. In addition, five dimensions have been identified under which ecotourism opportunities can be measured, including nature, environmental education rotection, sustainability, socio-cultural benefits, and tourist satisfaction. Existing scales or indices assess potential destinations qualitatively rather than quantitatively. In contrast, an index-based approach might help to solve the challenges of evaluating ecotourism opportunities as a CAS, as well as to quantitatively assess potential destinations to support decision-making related to ecotourism promotion.
Publisher: Informa UK Limited
Date: 03-06-2023
Publisher: Elsevier BV
Date: 09-2017
DOI: 10.1016/J.AAP.2017.05.026
Abstract: Recent developments in Western Australia's economy including widespread traffic congestion as well as road safety issues are increasingly becoming prominent. Previous studies relied on traditional statistical methods to investigate patterns and characteristics of motor vehicle crashes. Although useful, statistical analysis alone is incapable of providing a spatial context and is therefore unable to associate existing crash characteristics with a spatial distribution. To identify concentrations or "hotspots" of articulated heavy vehicle crashes in WA between the years 2001-2013, by using a spatial analysis approach. Spatial modelling and spatio-temporal analytical methods such as Emerging Hotspots were used to identify emerging hotspots on specific roads in Western Australia using the Integrated Road Information System (IRIS). The results suggest that the majority of articulated heavy vehicles crashes occurred in the vicinity or within the Perth metropolitan area. Based on spatial-temporal trend analyses, our findings highlight some regions that are emerging as areas of interest. This study was one of the first attempts to adopt a spatial analysis approach in studying heavy-vehicle crashes in Western Australia. Applying spatial methodologies to road safety data has the potential of obtaining previously undiscovered insights, which can be extended further, and provide future avenues to research in this field.
Publisher: Royal Society of Chemistry (RSC)
Date: 2015
DOI: 10.1039/C5CP03787E
Abstract: Principles of structure formation and adsorbate–adsorbate interactions in ionic liquid adlayers on metal surfaces were investigated in a comparative STM study on Ag(111) and Au(111) surfaces.
Publisher: Springer Science and Business Media LLC
Date: 11-08-2022
DOI: 10.1038/S41598-022-17878-6
Abstract: Assessing vulnerability to natural hazards is at the heart of hazard risk reduction. However, many countries such as Australia lack measuring systems to quantity vulnerability for hazard risk evaluation. Drawing on 41 indicators from multiple data sources at the finest spatial unit of the Australian census, we re-forged the Cutter’s classic vulnerability measuring framework by involving the ‘4D’ quantification of built environment ( ersity, design, density and distance), and constructed the first nationwide fine-grained measures of vulnerability for urban and rural locales, respectively. Our measures of vulnerability include five themes—(1) socioeconomic status (2) demographics and disability (3) minority and languages (4) housing characteristics and (5) built environment—that were further used to assess the inequality of vulnerability to three widely affected natural hazards in Australia (wildfires, floods, and earthquakes). We found the inequality of vulnerability in the affected areas of the three hazards in eight capital cities are more significant than that of their rural counterparts. The most vulnerable areas in capital cities were peri-urban locales which must be prioritised for hazard adaptation. Our findings contribute to the risk profiling and sustainable urban–rural development in Australia, and the broad understanding of place-based risk reduction in South Hemisphere.
Publisher: MDPI AG
Date: 19-05-2022
DOI: 10.3390/EN15103740
Abstract: Non-renewable-resource consumption and global greenhouse-gas (GHG) emissions are critical issues that pose a significant threat to sustainable development. Solar energy is a promising source to generate renewable energy and an appealing alternative electricity source for households. The primary goal of this research is to detect the rooftops that have no solar photovoltaic (PV) system deployed on them but that receive moderate to high solar-energy radiation using the Geographic Information System (GIS) and deep-learning techniques. Although various studies have been conducted on this subject, not many addressed these two issues simultaneously at a residential level. Identifying the installed solar PV systems in a large area can be expensive and time-consuming work if performed manually. Therefore, the deep-learning algorithm is an emerging alternative method to detect objects using aerial images. We employed the Single-Shot-Detector (SSD) model with the backbone of residual neural network 34 (ResNet34) to detect the solar PV systems and used GIS software to compute solar isolation and calculate the electricity production estimate (EPE) of each rooftop. Our results show that the SSD model detected 6010 solar panels on 4150 properties with an accuracy of 78% and observed that there were 176 Statistical Area 1s (SA1s) that had no rooftops with solar PV systems installed. Moreover, the total electricity production from the suitable area was estimated at over 929.8 Giga Watt-hours (GWhs) annually. Finally, the relation between solar-PV-system density and EPE was also identified using the bivariant correlation technique. Detecting the existing solar PV systems is useful in a broad range of applications including electricity-generation prediction, power-plant-production management, uncovering patterns between regions, etc. Examination of the spatial distribution of solar-energy potential in a region and performing an overlay analysis with socio-economic factors can help policymakers to understand the explanation behind the pattern and strategize the incentives accordingly.
Publisher: Elsevier BV
Date: 2017
Publisher: Oxford University Press (OUP)
Date: 18-06-2022
Abstract: This study establishes a novel empirical framework using machine learning techniques to measure the urban-regional disparity of the public’s mental health signals in Australia during the pandemic, and to examine the interrelationships amongst mental health, demographic and socioeconomic profiles of neighbourhoods, health risks and healthcare access. Our results show that the public’s mental health signals in capital cities were better than those in regional areas. The negative mental health signals in capital cities are associated with a lower level of income, more crowded living space, a lower level of healthcare availability and more difficulties in healthcare access.
Publisher: MDPI AG
Date: 20-08-2021
DOI: 10.3390/RS13163307
Abstract: Drought has significant impacts on the agricultural productivity and well-being of Pacific Island communities. In this study, a user-centred integrated early warning system (I-EWS) for drought was investigated for Papua New Guinea (PNG). The I-EWS combines satellite products (Standardised Precipitation Index and Vegetation Health Index) with seasonal probabilistic forecasting outputs (chance of exceeding median rainfall). Internationally accepted drought thresholds for each of these inputs are conditionally combined to trigger three drought early warning stages—”DROUGHT WATCH”, “DROUGHT ALERT” and “DROUGHT EMERGENCY”. The developed I-EWS for drought was used to examine the evolution of a strong El Niño-induced drought event in 2015 as well as a weaker La Niña-induced dry period in 2020. Examining the evolution of drought early warnings at a provincial level, it was found that tailored warning lead times of 3–5 months could have been possible for several impacted PNG provinces. These lead times would enable increasingly proactive drought responses with the potential for prioritised allocation of funds at a provincial level. The methodology utilised within this study uses inputs that are openly and freely available globally which indicates promising potential for adaptation of the developed user-centred I-EWS in other Pacific Island Countries that are vulnerable to drought.
Publisher: MDPI AG
Date: 03-01-2022
DOI: 10.3390/EN15010312
Abstract: Many countries have set a goal for a carbon neutral future, and the adoption of solar energy as an alternative energy source to fossil fuel is one of the major measures planned. Yet not all locations are equally suitable for solar energy generation. This is due to uneven solar radiation distribution as well as various environmental factors. A number of studies in the literature have used multicriteria decision analysis (MCDA) to determine the most suitable places to build solar power plants. To the best of our knowledge, no study has addressed the subject of optimal solar plant site identification for the Al-Qassim region, although developing renewable energy in Saudi Arabia has been put on the agenda. This paper developed a spatial MCDA framework catering to the characteristics of the Al-Qassim region. The framework adopts several tools used in Geographic Information Systems (GIS), such as Random Forest (RF) raster classification and model builder. The framework aims to ascertain the ideal sites for solar power plants in the Al-Qassim region in terms of the amount of potential photovoltaic electricity production (PVOUT) that could be produced from solar energy. For that, a combination of GIS and Analytical Hierarchy Process (AHP) techniques were employed to determine five sub-criteria weights (Slope, Global Horizontal Irradiance (GHI), proximity to roads, proximity to residential areas, proximity to powerlines) before performing spatial MCDA. The result showed that ‘the most suitable’ and ‘suitable’ areas for the establishment of solar plants are in the south and southwest of the region, representing about 17.53% of the study area. The ‘unsuitable’ areas account for about 10.17% of the total study area, which is mainly concentrated in the northern part. The rest of the region is further classified into ‘moderate’ and ‘restricted’ areas, which account for 46.42% and 25.88%, respectively. The most suitable area for potential solar energy, yields approximately 1905 Kwh/Kwp in terms of PVOUT. The proposed framework also has the potential to be applied to other regions nationally and internationally. This work contributes a reproducible GIS workflow for a low-cost but accurate adoption of a solar energy plan to achieve sustainable development goals.
Publisher: MDPI AG
Date: 09-02-2021
DOI: 10.3390/GERIATRICS6010016
Abstract: Stroke can adversely affect the coordination and judgement of drivers due to executive dysfunction, which is relatively common in the post-stroke population but often undetected. Quantitatively examining vehicle control performance in post-stroke driving becomes essential to inspect whether and where post-stroke older drivers are risky. To date, it is unclear as to which indicators, such as lane keeping or speed control, can differentiate the driving performance of post-stroke older drivers from that of normal (neurotypical) older drivers. By employing a case–control design using advanced vehicle movement tracking and analysis technology, this pilot study aimed to compare the variations in driving trajectory, lane keeping and speed control between the two groups of older drivers using spatial and statistical techniques. The results showed that the mean standard deviation of lane deviation (SDLD) in post-stroke participants was higher than that of normal participants in complex driving tasks (U-turn and left turn) but almost the same in simple driving tasks (straight line sections). No statistically significant differences were found in the speed control performance. The findings indicate that, although older drivers can still drive as they need to after a stroke, the decline in cognitive abilities still imposes a higher cognitive workload and more effort for post-stroke older drivers. Future studies can investigate post-stroke adults’ driving behaviour at more challenging driving scenarios or design driving intervention programs to improve their executive function in driving.
Publisher: Elsevier BV
Date: 08-2022
Publisher: Frontiers Media SA
Date: 06-04-2022
Abstract: An increase in energy demands and positive public acceptance of clean energy resources have contributed to a growing need for using solar energy in cities. Solar photovoltaic (PV) deployment relies on suitable locations with high solar energy potential. In the urban context, building rooftops are often considered one of the most available locations for solar PV installation. This work demonstrates a new geospatial-method for spatiotemporal modeling and mapping solar energy potential based on a high-resolution (0.2 m) digital surface model (DSM) and solar radiation dataset. The proposed method identifies building rooftops with a high solar energy potential by using the Solar Analyst (SA) model. The results show that 93.5% of the rooftop area has high solar energy potential in the study area. The annual averaged sum of solar irradiation values is estimated to be 1.36 MWh/m 2 . In addition, the study showed that sloped rooftops facing to the north received up to 30% more incoming solar radiation than other rooftops with different geometry and orientation. The results are validated using recorded energy output data from four existing solar PV systems in the study area. The return on the initial investment of PV systems installation is estimated to be from four to five years.
Publisher: Elsevier BV
Date: 10-2018
Publisher: Elsevier BV
Date: 12-2023
Publisher: Elsevier BV
Date: 03-2019
DOI: 10.1016/J.SCITOTENV.2018.11.223
Abstract: Guiding urban planners on the cooling returns of different configurations of urban vegetation is important to protect urban dwellers from adverse heat impacts. To this end, we estimated statistical models that fused multi-temporal very fine spatial (20 cm) and vertical (1 mm) resolution imagery, that captures the complexity of urban vegetation, with remotely sensed temperature data to assess how urban vegetation configuration influences urban temperatures. Perth, Western Australia, was used as a case-study for this analysis. Panel regression models showed that within a location an increase in tree and shrub cover has a larger cooling effect than grass coverage. On average, holding all else equal, an approximate 1 km
Publisher: MDPI AG
Date: 03-03-2023
DOI: 10.3390/LAND12030600
Abstract: Global environmental governance (GEG) is one of the world’s major attempts to address climate change issues through mitigation and adaptation strategies. Despite a significant improvement in GEG’s structural, human, and financial capital, the global commons are decaying at an unprecedented pace. Among the global commons, land has the largest share in GEG. Land use change, which is rooted in increasing populations and urbanization, has a significant role in greenhouse gas (GHG) emissions. As a response, land governance and, consequently, good land governance, have arisen as normative concepts emerging from a series of success factors (notably economic development, environmental conservation, and social justice) to achieve greater sustainability. However, global land governance has shown little success in helping GEG due to the lack of intellectual and flexible thinking over governing the land sector. Consequently, reforming land governance “in a smart way” is one of the most critical actions that could contribute to achieving GEG goals. Hence, we propose a smart land governance (SLG) system that will be well addressed, understood, and modeled in a systemic and dynamic way. A smart system may be smart enough to adapt to different contexts and intellectual responses in a timely fashion. Accordingly, SLG is able to promote shared growth and solve many land sector problems by considering all principles of good land governance. Therefore, in order to enhance adaptive land governance systems, efficient land administration and management are required. This study’s outcomes will raise the comprehension of the problems of land management, providing an excellent framework to help land planners and policy-makers, as well as the development of strategic principles with respect to the principal multidimensional components of SLG.
Publisher: Springer Science and Business Media LLC
Date: 31-01-2023
Publisher: MDPI AG
Date: 14-04-2022
DOI: 10.3390/RS14081903
Abstract: An approach to developing a blended satellite-rainfall dataset over Australia that could be suitable for operational use is presented. In this study, Global Satellite Mapping of Precipitation (GSMaP) satellite precipitation estimates were blended with station-based rain gauge data over Australia, using operational station data that has not been harnessed by other blended products. A two-step method was utilized. First, GSMaP satellite precipitation estimates were adjusted using rain gauge data through multiplicative ratios that were gridded using ordinary kriging. This step resulted in reducing dry biases, especially over topography. The adjusted GSMaP data was then blended with the Australian Gridded Climate Dataset (AGCD) rainfall analysis, an operational station-based gridded rain gauge dataset, using an inverse error variance weighting method to further remove biases. A validation that was performed using a 20-year range (2001 to 2020) showed the proposed approach was successful the resulting blended dataset displayed superior performance compared to other non-gauge-based datasets with respect to stations as well as displaying more realistic patterns of rainfall than the AGCD in areas with no rain gauges. The average mean absolute error (MAE) against station data was reduced from 0.89 to 0.31. The greatest bias reductions were obtained for extreme precipitation totals and over mountainous regions, provided sufficient rain gauge availability. The newly produced dataset supported the identification of a general positive bias in the AGCD over the north-west interior of Australia.
Publisher: MDPI AG
Date: 16-03-2023
Abstract: In this study, a comprehensive investigation into the inter-relationships among twelve atmospheric variables and their responses to precipitation was conducted. These variables include two Global Navigation Satellite Systems (GNSS) tropospheric products, eight weather variables and two time-varying parameters. Their observations and corresponding precipitation record over the period 2008–2019 were obtained from a pair of GNSS/weather stations in Hong Kong. Firstly, based on the correlation and regression analyses, the cross-relationships among the variables were systematically analyzed. Typically, the variables of precipitable water vapor (PWV), zenith total delay (ZTD), temperature, pressure, wet-bulb temperature and dew-point temperature have closer cross-correlativity. Next, the responses of these variables to precipitation of different intensities were investigated and some precursory information of precipitation contained in these variables was revealed. The lead times of using ZTD and PWV to detect heavy precipitation are about 8 h. Finally, by using the principal component analysis, it is shown that heavy precipitation can be effectively detected using these variables, among which, ZTD, PWV and cloud coverage play more prominent roles. The research findings can not only increase the utilization and uptake of atmospheric variables in the detection of precipitation, but also provide clues in the development of more robust precipitation forecasting models.
Publisher: Elsevier BV
Date: 04-2019
Publisher: Elsevier BV
Date: 2023
DOI: 10.1016/J.JENVMAN.2022.116663
Abstract: The warming trend over recent decades has already contributed to the increased prevalence of heat-vulnerable chronic diseases in many regions of the world. However, understanding the relationship between heat-vulnerable chronic diseases and heatwaves remains incomplete due to the complexity of such a relationship mingling with human society, urban and natural environments. Our study extends the Social Ecological Theory by constructing a tri-environmental conceptual framework (i.e., across social, built, and natural environments) and contributes to the first nationwide study of the relationship between heat-vulnerable chronic diseases and heatwaves in Australia. We utilize the random forest regression model to explore the importance of heatwaves and 48 tri-environmental variables that contribute to the prevalence of six types of heat-vulnerable diseases. We further apply the local interpretable model-agnostic explanations and the accumulated local effects analysis to interpret how the heat-disease nexus is mediated through tri-environments and varied across urban and rural space. The overall effect of heatwaves on diseases varies across disease types and geographical contexts (latitudes inland versus coast). The local heat-disease nexus follows a J-shape function-becoming sharply positive after a certain threshold of heatwaves-reflecting that people with the onset of different diseases have various sensitivity and tolerance to heatwaves. However, such effects are relatively marginal compared to tri-environmental variables. We propose a number of policy implications on reducing urban-rural disparity in healthcare access and service distribution, delineating areas, and identifying the variations of sensitivity to heatwaves across urban/rural space and disease types. Our conceptual framework can be further applied to examine the relationship between other environmental problems and health outcomes.
Publisher: Elsevier BV
Date: 09-2019
Publisher: Elsevier BV
Date: 09-2021
Publisher: IEEE
Date: 07-2019
Publisher: Elsevier BV
Date: 06-2021
Publisher: Informa UK Limited
Date: 04-05-2016
Publisher: Elsevier BV
Date: 04-2019
DOI: 10.1016/J.SCITOTENV.2018.12.308
Abstract: Due to the intensity of urban development around the world, there is an increasing body of studies attempting to investigate urban heat island (UHI) in various spatial and temporal scales. In surface heat urban island (SUHI) studies, extended periods of time, broader regions and local government area (LGA) level have become more crucial and will shed light on causes of UHI. Moreover, the spatial pattern and structure of SUHI will be useful for policy-makers to develop mitigation strategies. This study focused on three objectives. Firstly, analyzing land surface temperature (LST), normalized difference built-up (NDBI) and vegetation (NDVI) indices. Secondly, investigating interrelationships among LST, NDVI, and NDBI. Thirdly, identifying LST patterns in the Melbourne metropolitan area. These objectives were achieved through three different methods. The modified automatic mapping method for the first objective, the correlation analysis for the second, and spatial statistical methods for the third. The methodological innovations of this study were considering LGA in interrelationship analysis among LST, NDBI and NDVI, and calculation of NDVI for each acquisition date. The results indicated that the clustering pattern of LST expanded toward the north-west and south-east during the period of the study. Furthermore, the north-west part of the city has the highest positive (0.6) correlation between NDBI and LST, and the south-east part of the city has the lowest negative (-0.8) correlation between NDVI and LST. The most significant increase and decrease in mean LST happened respectively from January 6th to 22nd 2017, and January 14th to 30th January 2014. The temperature degree altered from 19.61 °C to 27.86 °C in inner western suburbs, and from 35.49 °C to 26.88 °C in most LGA's. These findings are critical for planners to localize UHI mitigation action plans, target hot spots in LGA's and allocate resources to respond to the adverse effect of UHI.
Publisher: Elsevier BV
Date: 09-2021
Publisher: Elsevier BV
Date: 07-2023
Publisher: MDPI AG
Date: 28-12-2021
Abstract: (1) Background: Evaluation of wind environments regarding pedestrian comfort may unveil potential hotspot areas, particularly in the context of the rapid urban development in China since the 1990s. (2) Method: With primary schools in Nanjing as case studies, the authors simulated the wind environment of schoolyards with the computational fluid dynamics (CFD) approach and evaluated relevant wind comfort criteria. (3) Results: The study showed that the comfortable wind environment of schoolyards generally expanded in three primary schools in summer and winter, and wind speed and the comfortable wind level decreased in some outdoor schoolyard spaces. The results also indicate that the mean wind speed of the schoolyards did not linearly correlate to the building density either within or outside the schools. An increase in the building height of the primary schools could improve the wind comfort of the schoolyard, but the increased building height in the vicinity may worsen the wind comfort in the schools. Meanwhile, a lift-up or step-shaped building design for schools can improve wind comfort in schoolyards. (4) Conclusions: This study provided simulated results and an approach for urban designers to evaluate and improve the wind environment for school children’s outdoor activities.
Publisher: Informa UK Limited
Date: 09-2013
Publisher: MDPI AG
Date: 16-08-2022
DOI: 10.3390/RS14163971
Abstract: Soil moisture (SM) is critical in monitoring the time-lagged impacts of agrometeorological drought. In Australia and several south-west Pacific Small Island Developing States (SIDS), there are a limited number of in situ SM stations that can adequately assess soil-water availability in a near-real-time context. Satellite SM datasets provide a viable alternative for SM monitoring and agrometeorological drought provision in these regions. In this study, we investigated the performance of Soil Moisture Active Passive (SMAP), Soil Moisture and Ocean Salinity (SMOS), Soil Moisture Operational Products System (SMOPS), SM from the Advanced Microwave Scanning Radiometer 2 (AMSR-2) and SM from the Advanced Scatterometer (ASCAT) over Australia and south-west Pacific SIDS. Products were first evaluated in Australia, given the presence of several in-situ SM monitoring stations and a state-of-the-art hydrological model—the Australian Water Resources Assessment Landscape modelling system (AWRA-L). We further investigated the accuracy of SM satellite datasets in Australia and the south-west Pacific through Triple Collocation analysis with two other SM reference datasets—ERA5 reanalysis SM data and model data from the Global Land Data Assimilation System (GLDAS) dataset. All datasets have differing observation periods ranging from 1911-now, with a common period of observations between 2015–2021. Results demonstrated that ASCAT and SMOS were consistently superior in their performance. Analysis in the six south-west Pacific SIDS indicated reduced performance for all products, with ASCAT and SMOS still performing better than others for most SIDS with median R values ranging between 0.3–0.9. We conducted a case study of the 2015 El Niño and Positive Indian Ocean Dipole-induced drought in Papua New Guinea. It was shown that ASCAT is a valuable dataset indicative of agrometeorological drought for the nation, highlighting the value of using satellite SM products to provide early warning of drought in data-sparse regions in the south-west Pacific.
Publisher: Elsevier BV
Date: 05-2023
Publisher: Springer Science and Business Media LLC
Date: 30-11-2022
DOI: 10.1038/S41598-022-25255-6
Abstract: Rainfall estimation over large areas is important for a thorough understanding of water availability, influencing societal decision-making, as well as being an input for scientific models. Traditionally, Australia utilizes a gauge-based analysis for rainfall estimation, but its performance can be severely limited over regions with low gauge density such as central parts of the continent. At the Australian Bureau of Meteorology, the current operational monthly rainfall component of the Australian Gridded Climate Dataset (AGCD) makes use of statistical interpolation (SI), also known as optimal interpolation (OI) to form an analysis from a background field of station climatology. In this study, satellite observations of rainfall were used as the background field instead of station climatology to produce improved monthly rainfall analyses. The performance of these monthly datasets was evaluated over the Australian domain from 2001 to 2020. Evaluated over the entire national domain, the satellite-based SI datasets had similar to slightly better performance than the station climatology-based SI datasets with some in idual months being more realistically represented by the satellite-SI datasets. However, over gauge-sparse regions, there was a clear increase in performance. For a representative sub-domain, the Kling-Gupta Efficiency (KGE) value increased by + 8% (+ 12%) during the dry (wet) season. This study is an important step in enhancing operational rainfall analysis over Australia.
Publisher: Informa UK Limited
Date: 05-09-2019
Publisher: Copernicus GmbH
Date: 17-10-2022
DOI: 10.5194/ISPRS-ARCHIVES-XLVIII-4-W5-2022-175-2022
Abstract: Abstract. To tackle the increasing issue of heat risk in Australia and pressure of population growth, this project aimed to establish a first nationwide dynamic and interactive heat vulnerability assessment toolkit. The toolkit integrated multiple data sources, cloud computing, and Web GIS technologies to deliver cool intelligence for more heat resilient Australian cities and suburbs. A cloud-based open-source tool, iGEE, was first developed to derive land surface temperature (LST), NDBI (normalized difference built-up index) and NDVI (normalized difference vegetation index) from multiple satellite imagery on Google Earth Engine (GEE), a no-code web application allowing users to retrieve satellite data for a large area at fine scales. Following that, a python-based desktop app was then developed to calculate an integrated Heat Vulnerability Index (iHVI) for any cities and area at a fine scale of Statistical Area 1 (SA1). The iHVI toolkit allows users to construct heat sensitivity, heat adaptive capability indicators, and composite heat vulnerability index, which enables modelling of the relationships between heat, environmental and socioeconomic factors.
Publisher: Copernicus GmbH
Date: 27-10-2022
DOI: 10.5194/ISPRS-ARCHIVES-XLVIII-3-W2-2022-43-2022
Abstract: Abstract. The building footprint is crucial for a volumetric 3D representation of a building that is applied in urban planning, 3D city modeling, cadastral and topographic map generation. Aerial laser scanning (ALS) has been recognized as the most suitable means of large-scale 3D point cloud data (PCD) acquisition. PCD can produce geometric detail of a scanned surface. However, it is almost impossible to get point clouds without noise and outliers. Besides, data incompleteness and occlusions are two common phenomena for PCD. Most of the existing methods for building footprint extraction employ classification, segmentation, voting techniques (e.g., Hough-Transform or RANSAC), or Principal Component Analysis (PCA) based methods. It is known that classical PCA is highly sensitive to outliers, even RANSAC which is known as a robust technique for shape detection is not free from outlier effects. This paper presents a novel algorithm that employs MCMD (maximum consistency within minimum distance), MSAC (a robust variant of RANSAC) and a robust regression to extract reliable building footprints in the presence of outliers, missing points and irregular data distributions. The algorithm is successfully demonstrated through two sets of ALS PCD.
Publisher: Elsevier BV
Date: 10-2020
Publisher: IEEE
Date: 07-2022
Publisher: Informa UK Limited
Date: 2021
Publisher: IEEE
Date: 03-11-2021
Publisher: Elsevier BV
Date: 2017
Publisher: Elsevier BV
Date: 04-2018
DOI: 10.1016/J.AAP.2018.01.019
Abstract: Visual information for a driver is predominant during driving. Linking drivers' visual search patterns with motor behaviour helps understand how drivers perceived spatial and hazardous information to regulate their physical movements. Visual-motor coordination performance can be a sensitive indicator for driver competency assessment. Due to age-related cognitive decline, older drivers are likely inefficient in visual-motor coordination. While poor visual-motor coordination can cause risky behaviour behind the wheel, it is yet challenging to examine it owing to the complexity of driving behaviour. By reviewing how vision guides driving, we proposed a gaze-based integrated driving assessment approach. The empirical data were from 38 older drivers aged 60 to 81 years, who completed an on-road driving assessment recorded by eye tracking and vehicle movement tracking. Their visual search attributes were extracted from eye tracking video frames and linked to vehicle positions. Driving data, drivers' cognitive condition and driving section were encapsulated into an integrated database, allowing interrogating multi-faceted driver-vehicle-environment interactions. Exploratory analysis results show that older drivers' performed different visual search patterns at roundabout and intersection manoeuvres. Older drivers with better executive function skills performed more frequent eye fixations on the curves and inside vehicle features. The investigation of visual-motor coordination performance demonstrated the feasibility and effectiveness of using the integrated approach in assessing older drivers' performance.
Publisher: Elsevier BV
Date: 04-2021
Publisher: Elsevier BV
Date: 02-2018
Publisher: Copernicus GmbH
Date: 07-02-2023
DOI: 10.5194/NHESS-23-553-2023
Abstract: Abstract. Climate change is increasing the frequency and intensity of natural hazards, causing disastrous impacts on vulnerable communities. Pacific Small Island Developing States (SIDS) are of particular concern, requiring resilient disaster risk management consisting of two key elements: proactivity and suitability. Drought risk knowledge can inform resilient risk management, but it is currently underexplored in Pacific SIDS, particularly in the highly vulnerable nation of Papua New Guinea (PNG). A tailored, meaning highly specific to the area under investigation, drought risk assessment methodology is key for expanding risk knowledge in vulnerable communities. A semi-dynamic and tailored drought risk assessment methodology to be utilised in PNG was developed in this research. Representative hazard, vulnerability, and exposure indicators were selected, and integrated geographic information system (GIS) processes were used to produce hazard, vulnerability, exposure, and risk indices and maps. The validity of the risk assessment was investigated with a retrospective risk assessment of drought in PNG (from 2014–2020) paired with a literature assessment (as a ground-truth source), and a sensitivity analysis. The preliminary drought risk assessment methodology demonstrated in this study was overall deemed valid and robust, with supplementary improvements proposed for consideration in future investigation. The developed methodology makes strides in addressing methodological knowledge gaps in drought risk assessment, for global assessments and those specific for PNG, and demonstrates the potential for risk assessment to inform resilient drought management practices in at-risk areas. Overall, the results of this study directly contribute to enhancing provincial drought risk knowledge in PNG.
Publisher: University of Bern
Date: 28-08-2016
DOI: 10.16910/JEMR.9.6.2
Abstract: Visual capacity generally declines as people age, yet its impact on visual search patterns along different road sections of actual driving still remains undocumented. In this on-road driving study, we simultaneously recorded 30 older drivers’ eye movement and precise vehicle movement trajectories. The vehicle positions were linked to every eye fixation of in idual drivers so that we know the locations of a driver's gaze origin in geospatial coordinates. Spatial distribution pattern of drivers’ eye fixations (duration and frequency) were then analysed. We further investigated the associations between older drivers’ visual capacity (processing speed, ided and selective attention) and their eye fixation patterns in various driving manoeuvres. The results indicate that driving scenarios have significant impact on older drivers’ visual patterns. Older drivers performed more frequent eye fixations when manoeuvreing through roundabouts, while they tended to fixate on certain objects much longer during straight road driving. The key findings show that the processing speed and ided attention of older drivers were associated with their eye fixations at complex right-turns drivers with a lower capacity for selective attention performed less frequent eye fixations at roundabout manoeuvres. This study has also demonstrated that visualisation and spatial statistics are effective and intuitive approaches in eye movement analysis.
Publisher: Copernicus GmbH
Date: 18-11-2020
DOI: 10.5194/ISPRS-ARCHIVES-XLIV-3-W1-2020-139-2020
Abstract: Abstract. Australia frequently experiences extended periods of severe droughts which have a significant negative impact on populations and economy. To improve preparedness for drought, decision-support tools which provide comprehensive information about current dry conditions are essential. In this paper, we present a conceptual design for a Drought Risk Analyser (DRA) – web-based information App for drought risk mapping developed using geographic information system (GIS). The developed DRA is based on combining Drought Hazard/Vulnerability/Exposure Indices (DHI, DVI and DEI respectively) into a final Drought Risk Index (DRI) for total of 542 Local Government Areas (LGA) in Australia. Drought indicators selected to compute drought hazard – the Standardised Precipitation Index (SPI), the Vegetation Health Index (VHI) and Soil Moisture – were obtained through the World Meteorological Organization (WMO) Space-based Weather and Climate Extremes Monitoring (SWCEM) international initiative. Australian Bureau of Statistics (ABS) census data were used to develop the drought-related population vulnerability index – DVI. Australian national Digital Elevation Model and catchment scale land use data were used to calculate the DEI. Implemented functionality of the designed DRA is illustrated using a case study for the 2019 drought in Australia. The DRA App will be beneficial for Australian farmers and rural communities to assist with decision making, as well as for LGA planners to gain insights on current state of drought risk at both local and national levels. The developed methodology of using space-based observations for assessing drought hazard could be applied for developing similar web-based information tools in drought-prone areas of other countries.
Publisher: Springer Science and Business Media LLC
Date: 06-2018
Publisher: Elsevier BV
Date: 02-2021
Start Date: 2023
End Date: 12-2025
Amount: $491,013.00
Funder: Australian Research Council
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