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The article discusses on the need of marine biodiversity as tool for conservation planning and prioritization for highly threatened environments for coral reefs. Using image processing or interpretation for coral reefs of Great Barrier Reef, Australia an index of image-derived spatial heterogeneity was used. It demonstrated the possibility of easily and rapidly exploiting spatial information, to complement existing methods for inferring diversity and abundance patterns among fish communities.

In a recent Australian homicide, trace soil on the victim's clothing suggested she was initially attacked in her front yard and not the park where her body was buried. However the important issue that emerged during the trial was how soil was transferred to her clothing. This became the catalyst for designing a range of soil transference experiments (STEs) to study, recognise and classify soil patterns transferred onto fabric when a body is dragged across a soil surface. Soil deposits of interest in this murder were on the victim's bra and this paper reports the results of anthropogenic soil transfer to bra-cups and straps caused by dragging. Transfer patterns were recorded by digital photography and photomicroscopy. Eight soil transfer patterns on fabric, specific to dragging as the transfer method, appeared consistently throughout the STEs. The distinctive soil patterns were largely dependent on a wide range of soil features that were measured and identified for each soil tested using X-ray Diffraction and Non-Dispersive Infra-Red analysis. Digital photographs of soil transfer patterns on fabric were analysed using image processing software to provide a soil object-oriented classification of all soil objects with a diameter of 2 pixels and above transferred. Although soil transfer patterns were easily identifiable by naked-eye alone, image processing software provided objective numerical data to support this traditional (but subjective) interpretation. Image software soil colour analysis assigned a range of Munsell colours to identify and compare trace soil on fabric to other trace soil evidence from the same location; without requiring a spectrophotometer. Trace soil from the same location was identified by linking soils with similar dominant and sub-dominant Munsell colour peaks. Image processing numerical data on the quantity of soil transferred to fabric, enabled a relationship to be discovered between soil type, clay mineralogy (smectite), particle size and soil moisture content that would not have been possible otherwise. Soil type (e.g. Anthropogenic, gravelly sandy loam soil or Natural, organic-rich soil), clay mineralogy (smectite) and soil moisture content were the greatest influencing factors in all the dragging soil transference tests (both naked eye and measured properties) to explain the eight categories of soil transference patterns recorded. This study was intended to develop a method for dragging soil transference laboratory experiments and create a baseline of preliminary soil type/property knowledge. Results confirm the need to better understand soil behaviour and properties of clothing fabrics by further testing of a wider range of soil types and clay mineral properties. [ABSTRACT FROM AUTHOR]

Breast microcalcifications are an important primary radiological indicator of breast cancer. However, microcalcification classification and diagnosis may be still challenging for radiologists due to limitations of the standard 2D mammography technique, including spatial and contrast resolution. In this study, we propose an approach to improve the detection of microcalcifications in propagation-based phase-contrast X-ray computed tomography of breast tissues. Five fresh mastectomies containing microcalcifications were scanned at different X-ray energies and radiation doses using synchrotron radiation. Both bright-field (i.e. conventional phase-retrieved images) and dark-field images were extracted from the same data sets using different image processing methods. A quantitative analysis was performed in terms of visibility and contrast-to-noise ratio of microcalcifications. The results show that while the signal-to-noise and the contrast-to-noise ratios are lower, the visibility of the microcalcifications is more than two times higher in the dark-field images compared to the bright-field images. Dark-field images have also provided more accurate information about the size and shape of the microcalcifications. [ABSTRACT FROM AUTHOR]

The TROPOspheric Monitoring Instrument (TROPOMI) onboard the Sentinel-5 Precursor (S-5P) satellite, launched in 2017, measures the total column concentration of the trace gas Carbon Monoxide (CO) daily on a global scale and at a high spatial resolution of 7×7 km², improved to 5.5×7 km² in August 2019. The TROPOMI observations show plumes of CO due to localized CO emissions from industrial sources and biomass burning. In this paper, we quantify these CO emissions for biomass burning by an automated algorithm, APE, to detect plumes and quantify the CO emission rate using cross-sectional flux method. Furthermore, the influence of a constant and a varying plume height in downwind direction on emissions is investigated and algorithm uncertainties are quantified. The VIIRS active fire data in conjunction with the TROPOMI CO datasets is used to identify fires and the fire locations. Then, an automated plume detection algorithm using traditional image processing algorithms is developed and utilized to identify plumes. For these plumes, the emission rate is estimated by the cross10 section flux method at three different plume heights. The first two are constant plume heights at a 100 m and an IS4FIRES injection height from Global Fire Assimilation System. And the last one is a varying plume height in downwind direction. A 3D Lagrangian model is used to simulate tracer particles where the source locations for the simulation are based on the VIIRS fire counts and IS4FIRES injection height. 3D velocities at 137 model levels (ERA5) are utilized to simulate tracer particles. We demonstrate the quality and validity of our automated approach by investigating biomass burning events and their emissions for Australia on Oct 2019 and the US on Sept 2020. A total of 110 and 31 individual fire plumes in Australia and the US, respectively were detected and their emissions estimated. The emissions were severely under-predicted and negative for 11 cases when based on constant plume height of 100 m compared to emissions based on varying plume height. Furthermore, the effect of the changing plume height in downwind direction on the emission estimate compared to emissions from constant IS4FIRES plume height was minor as 124 cases are found to have emission variation less than 10%. However, we were able to identify several cases where the flux estimates become more reliable with varying plume height. Thus, the varying plume height in downwind direction is considered for the automated algorithm. The cross-section flux method is found to have an uncertainty of 38% in one of the idealized cases. However, overall uncertainty of the algorithm is difficult to quantify as conditions for each fire are unique. [ABSTRACT FROM AUTHOR]

This paper explores the potential of machine learning algorithms for weed and crop classification from UAV images. The identification of weeds in crops is a challenging task that has been addressed through orthomosaicing of images, feature extraction and labelling of images to train machine learning algorithms. In this paper, the performances of several machine learning algorithms, random forest (RF), support vector machine (SVM) and k-nearest neighbours (KNN), are analysed to detect weeds using UAV images collected from a chilli crop field located in Australia. The evaluation metrics used in the comparison of performance were accuracy, precision, recall, false positive rate and kappa coefficient. MATLAB is used for simulating the machine learning algorithms; and the achieved weed detection accuracies are 96% using RF, 94 % using SVM and 63 % using KNN. Based on this study, RF and SVM algorithms are efficient and practical to use, and can be implemented easily for detecting weed from UAV images. [ABSTRACT FROM AUTHOR]

This paper presents a new method for estimating nearshore wave height from a digital video sequence. The method identifies main wave breaking zones in the video records and estimates the height of breaking waves inside the detected breaking zones. A geometric rectification is applied to the resulting estimation to convert the height measurement from image pixels to meters. The validation of the algorithm was undertaken over three months at Surfers Paradise, Australia. The performance of the algorithm was demonstrated to be comparable with that of buoy-measured wave height, as well as manual estimates of the onshore wave height by a surf reporter. The results indicate that the method can be used as a cost-effective tool for long-term monitoring of nearshore wave conditions. [ABSTRACT FROM PUBLISHER]

Art and science show progress together and they are one of the most important parameters of development levels of countries, nations and societies. Moreover, art generates new concepts, environments and tools with the contributions of science and technology. Cartoon as an art discipline, as for all other fields of art, needs to be defined through new approaches such as Wavelet Transform and Neural Networks Methods which are commonly being used in image processing, compression, classification in various medical and biomedical fields. In this study, it is planned to apply such digital and analytical methods on Turkish and Australian cartoon data. Due to parallelism on drawing styles and characteristics of same periods' or following periods' cartoons of artists; physical classification and separation of cartoons may become harder. In addition, criticisms on cartoons commonly are to be interpretative without exact analytical indicators. [ABSTRACT FROM AUTHOR]

Abstract: We applied a texture-based flow visualisation technique to a numerical hydrodynamic model of the Pumicestone Passage in southeast Queensland, Australia. The quality of the visualisations using our flow visualisation tool, are compared with animations generated using more traditional drogue release plot and velocity contour and vector techniques. The texture-based method is found to be far more effective in visualising advective flow within the model domain. In some instances, it also makes it easier for the researcher to identify specific hydrodynamic features within the complex flow regimes of this shallow tidal barrier estuary as compared with the direct and geometric based methods. [Copyright &y& Elsevier]

The diagnostic image must be carried out with the minimum necessary exposure of the patient that allows the objective of the diagnosis to be satisfactorily achieved. For this reason, diagnostic reference levels emerge and are established as dynamic tools to help optimize radiation protection, contribute to the standardization of practices and strengthen culture of safety, without compromising the clinical purpose of each examination or process. The objective of this work is to provide an updated overview of the establishment and use of these levels for nuclear medicine and hybrid imaging. It is identified that to establish and use them properly, trained personnel and coordination and collaboration activities are required among multiple actors, including medical services, health authorities, professional organizations and regulatory bodies. The accelerated development of technology generally exceeds the change in regulations, so these levels must be updated periodically, in order to fulfil their role as a guide and spur for optimization. The worldwide expansion of hybrid technologies and their growing use are a phenomenon of the last decade, so the establishment of these levels for such technologies has not been consolidated, although countries such as the United Kingdom and Australia show solid steps in this address. Research has been carried out with phantoms and directly with patients, the latter with a more useful contribution of information. The installation of hybrid equipment in Cuba demands this study, hence its importance. [ABSTRACT FROM AUTHOR]

The article focuses on the ethics of the press photographers in Australia. It says that not all of the ethical considerations of photographers are confined to digital imaging. It adds that the result of the survey of the Australian photographers suggests they are content to take photographs and leave the ethical issues to those who make publishing decisions.

The article discusses the impacts of the electronic revolution on press photography in Australia in 1992. It mentions that implications of such development in the country for work practices, education and training, and ethics were criticised in the U.S. where computer enhancement and image manipulation were becoming a big issue. The effects of the new technology on the belief that the photographic image is a reflection of reality is investigated.

The article focuses on the study conducted by Adrian Dyer from RMIT University in Australia regarding the visual processing of bees. It mentions the two theories based on the study of Dier, such as the ability of bees in learning shapes of an image using their retina and its comparison to the images they previously viewed. Moreover, the other theory reveals the ability of bees' brain in more complex visual processing.

Study Question: Can an artificial intelligence (AI)-based model predict human embryo viability using images captured by optical light microscopy?Summary Answer: We have combined computer vision image processing methods and deep learning techniques to create the non-invasive Life Whisperer AI model for robust prediction of embryo viability, as measured by clinical pregnancy outcome, using single static images of Day 5 blastocysts obtained from standard optical light microscope systems.What Is Known Already: Embryo selection following IVF is a critical factor in determining the success of ensuing pregnancy. Traditional morphokinetic grading by trained embryologists can be subjective and variable, and other complementary techniques, such as time-lapse imaging, require costly equipment and have not reliably demonstrated predictive ability for the endpoint of clinical pregnancy. AI methods are being investigated as a promising means for improving embryo selection and predicting implantation and pregnancy outcomes.Study Design, Size, Duration: These studies involved analysis of retrospectively collected data including standard optical light microscope images and clinical outcomes of 8886 embryos from 11 different IVF clinics, across three different countries, between 2011 and 2018.Participants/materials, Setting, Methods: The AI-based model was trained using static two-dimensional optical light microscope images with known clinical pregnancy outcome as measured by fetal heartbeat to provide a confidence score for prediction of pregnancy. Predictive accuracy was determined by evaluating sensitivity, specificity and overall weighted accuracy, and was visualized using histograms of the distributions of predictions. Comparison to embryologists' predictive accuracy was performed using a binary classification approach and a 5-band ranking comparison.Main Results and the Role Of Chance: The Life Whisperer AI model showed a sensitivity of 70.1% for viable embryos while maintaining a specificity of 60.5% for non-viable embryos across three independent blind test sets from different clinics. The weighted overall accuracy in each blind test set was >63%, with a combined accuracy of 64.3% across both viable and non-viable embryos, demonstrating model robustness and generalizability beyond the result expected from chance. Distributions of predictions showed clear separation of correctly and incorrectly classified embryos. Binary comparison of viable/non-viable embryo classification demonstrated an improvement of 24.7% over embryologists' accuracy (P = 0.047, n = 2, Student's t test), and 5-band ranking comparison demonstrated an improvement of 42.0% over embryologists (P = 0.028, n = 2, Student's t test).Limitations, Reasons For Caution: The AI model developed here is limited to analysis of Day 5 embryos; therefore, further evaluation or modification of the model is needed to incorporate information from different time points. The endpoint described is clinical pregnancy as measured by fetal heartbeat, and this does not indicate the probability of live birth. The current investigation was performed with retrospectively collected data, and hence it will be of importance to collect data prospectively to assess real-world use of the AI model.Wider Implications Of the Findings: These studies demonstrated an improved predictive ability for evaluation of embryo viability when compared with embryologists' traditional morphokinetic grading methods. The superior accuracy of the Life Whisperer AI model could lead to improved pregnancy success rates in IVF when used in a clinical setting. It could also potentially assist in standardization of embryo selection methods across multiple clinical environments, while eliminating the need for complex time-lapse imaging equipment. Finally, the cloud-based software application used to apply the Life Whisperer AI model in clinical practice makes it broadly applicable and globally scalable to IVF clinics worldwide.Study Funding/competing Interest(s): Life Whisperer Diagnostics, Pty Ltd is a wholly owned subsidiary of the parent company, Presagen Pty Ltd. Funding for the study was provided by Presagen with grant funding received from the South Australian Government: Research, Commercialisation and Startup Fund (RCSF). 'In kind' support and embryology expertise to guide algorithm development were provided by Ovation Fertility. J.M.M.H., D.P. and M.P. are co-owners of Life Whisperer and Presagen. Presagen has filed a provisional patent for the technology described in this manuscript (52985P pending). A.P.M. owns stock in Life Whisperer, and S.M.D., A.J., T.N. and A.P.M. are employees of Life Whisperer. [ABSTRACT FROM AUTHOR]

Aiming at the problems of high stochasticity and volatility of power loads as well as the difficulty of accurate load forecasting, this paper proposes a power load forecasting method based on CEEMDAN (Completely Integrated Empirical Modal Decomposition) and TCN-LSTM (Temporal Convolutional Networks and Long-Short-Term Memory Networks). The method combines the decomposition of raw load data by CEEMDAN and the spatio-temporal modeling capability of TCN-LSTM model, aiming to improve the accuracy and stability of forecasting. First, the raw load data are decomposed into multiple linearly stable subsequences by CEEMDAN, and then the sample entropy is introduced to reorganize each subsequence. Then the reorganized sequences are used as inputs to the TCN-LSTM model to extract sequence features and perform training and prediction. The modeling prediction is carried out by selecting the electricity compliance data of New South Wales, Australia, and compared with the traditional prediction methods. The experimental results show that the algorithm proposed in this paper has higher accuracy and better prediction effect on load forecasting, which can provide a partial reference for electricity load forecasting methods. [ABSTRACT FROM AUTHOR]

The estimation of water quality properties through satellite remote sensing relies on (1) the optical characteristics of the water body, (2) the resolutions (spatial, spectral, radiometric and temporal) of the sensor and (3) algorithm(s) applied. More than 80% of global water bodies fall under Case I (open ocean) waters, dominated by scattering and absorption associated with phytoplankton in the water column. Globally, previous studies show significant correlations between satellite-based retrieval methods and field measurements of absorbing and scattering constituents, while limited research from Australian coastal water bodies appears. This study presents a methodology to extract chlorophyll a properties from surface waters from near-coastal environments, within 2 km of coastline, in Tasmania, south-eastern Australia. We use general purpose, global, long-time series, multi-spectral satellite data, as opposed to ocean colour-specific sensor data. This approach may offer globally applicable tools for combining global satellite image archives with in situ field sensors for water quality monitoring. To enable applications from local to global scales, a cloud-based geospatial analysis workflow was developed and tested on several sites. This work represents the initial stage in developing a semi-automated near-coastal water-quality workflow using easily accessed, fully corrected global multi-spectral datasets alongside large-scale computation and delivery capabilities. Our results indicated a strong correlation between the in situ chlorophyll concentration data and blue-green band ratios from the multi-spectral sensor. In line with published research, environment-specific empirical models exhibited the highest correlations between in situ and satellite measurements, underscoring the importance of tailoring models to specific coastal waters. Our findings may provide the basis for developing this workflow for other sites in Australia. We acknowledge the use of general purpose multi-spectral data such as the Sentinel-2 and Landsat Series, their corrections and algorithms may not be as accurate and precise as ocean colour satellites. The data we are using are more readily accessible and also have true global coverage with global historic archives and regular, global collection will continue at least 10 years in the future. Regardless of sensor specifications, the retrieval method relies on localised algorithm calibration and validation using in situ measurements, which demonstrates close-to-realistic outputs. We hope this approach enables future applications to also consider these globally accessible and regularly updated datasets that are suited to coastal environments. [ABSTRACT FROM AUTHOR]

Wetlands are integral components of agricultural landscapes, providing a wide range of ecological, economic, and social benefits essential for sustainable development and rural livelihoods. Globally, they are vulnerable ecological assets facing several significant threats including water extraction and regulation, land clearing and reclamation, and climate change. Classification and mapping of wetlands in agricultural landscapes is crucial for conserving these ecosystems to maintain their ecological integrity amidst ongoing land-use changes and environmental pressures. This study aims to establish a robust framework for wetland classification and mapping in intensive agricultural landscapes using time series of Sentinel-2 imagery, with a focus on the Gwydir Wetland Complex situated in the northern Murray–Darling Basin—Australia's largest river system. Using the Google Earth Engine (GEE) platform, we extracted two groups of predictors based on six vegetation indices time series calculated from multi-temporal Sentinel-2 surface reflectance (SR) imagery: the first is statistical features summarizing the time series and the second is phenological features based on harmonic analysis of time series data (HANTS). We developed and evaluated random forest (RF) models for each level of classification with combination of different groups of predictors. Our results show that RF models involving both HANTS and statistical features perform strongly with significantly high overall accuracy and class-weighted F1 scores (p < 0.05) when comparing with models with either statistical or HANTS variables. While the models have excellent performance (F-score greater than 0.9) in distinguishing wetlands from other landcovers (croplands, terrestrial uplands, and open waters), the inter-class discriminating power among wetlands is class-specific: wetlands that are frequently inundated (including river red gum forests and wetlands dominated by common reed, water couch, and marsh club-rush) are generally better identified than the ones that are flooded less frequently, such as sedgelands and woodlands dominated by black box and coolabah. This study demonstrates that HANTS features extracted from time series Sentinel data can significantly improve the accuracy of wetland mapping in highly fragmentated agricultural landscapes. Thus, this framework enables wetland classification and mapping to be updated on a regular basis to better understand the dynamic nature of these complex ecosystems and improve long-term wetland monitoring. [ABSTRACT FROM AUTHOR]

The 2012 satellite ASTER geoscience maps of Australia were designed to provide public, web-accessible, and spatially comprehensive surface mineralogy for improved mapping and solutions to geoscience challenges. However, a number of the 2012 products were clearly compromised by variable green and/or dry vegetation cover. Here, we show a strategy to first estimate and then unmix the contributions of both these vegetation components to leave, as residual, the target surface mineralogy. The success of this unmixing process is validated by (i) visual suppression/removal of the regional climate and/or local fire-scar vegetation patterns; and (ii) pixel values more closely matching field sample data. In this process, we also found that the 2012 spectral indices used to gauge the AlOH content, AlOH composition, and water content can be improved. The updated (new indices and vegetation unmixed) maps reveal new geoscience information, including: (i) regional "wet" and "dry" zones that appear to express "deep" geological characters often expressed through thick regolith cover, with one zone over the Yilgarn Craton spatially anti-correlated with Archaean gold deposits; (ii) a ~1000 km wide circular feature over the Lake Eyre region defined by a rim of abundant "muscovite" that appears to coincide with opal deposits; (iii) a N–S zonation across the western half of the continent defined by abundant muscovite in the south and kaolinite in the north, which appears to reflect opposing E ↔ W aeolian sediment transport directions across the high-pressure belt; (iv) various paleo-drainage networks, including those over aeolian sand covered the "lowlands" of the Canning Basin, which are characterized by low AlOH content, as well as those over eroding "uplands", such as the Yilgarn Craton, which have complicated compositional patterns; and (v) a chronological history of Miocene barrier shorelines, back-beach lagoons, and alluvial fans across the Eucla Basin, which, to date, had proved elusive to map using other techniques, with potential implications for heavy mineral sand exploration. Here, we explore the latter three issues. [ABSTRACT FROM AUTHOR]

Canid species are highly adaptable, including to urban and peri‐urban areas, where they can come into close contact with people. Understanding the mechanisms of wild canid population persistence in these areas is key to managing any negative impacts. The resource dispersion hypothesis predicts that animal density increases and home range size decreases as resource concentration increases, and may help to explain how canids are distributed in environments with an urban‐natural gradient. In Australia, dingoes have adapted to human presence, sometimes living in close proximity to towns. Using a targeted camera trap survey and spatial capture‐recapture models, we estimated spatial variation in the population density and detection rates of dingoes on Worimi Country in the Great Lakes region of the NSW coast. We tested whether dingo home range and population densities varied across a gradient of human population density, in a mixed‐use landscape including, urban, peri‐urban, and National Park environs. We found human population density to be a strong driver of dingo density (ranging from 0.025 to 0.433 dingoes/km2 across the natural‐urban gradient), and to have a negative effect on dingo home range size. The spatial scale parameter changed depending on survey period, being smaller in the peak tourism period, when human population increases in the area, than in adjacent survey periods, potentially indicating reduced home range size when additional resources are available. Our study highlights the potential value of managing anthropogenic resource availability to manage carnivore densities and potential risk of human‐carnivore interactions. [ABSTRACT FROM AUTHOR]

Reservoir heterogeneities play a crucial role in governing reservoir performance and management. Traditionally, detailed and inter-well heterogeneity analyses are commonly performed by mapping seismic facies change in the seismic data, which is a time-intensive task. Many researchers have utilized a robust Grey-level co-occurrence matrix (GLCM)-based texture attributes to map reservoir heterogeneity. However, these attributes take seismic data as input and might not be sensitive to lateral lithology variation. To incorporate the lithology information, we have developed an innovative impedance-based texture approach using GLCM workflow by integrating 3D acoustic impedance volume (a rock propertybased attribute) obtained from a deep convolution network-based impedance inversion. Our proposed workflow is anticipated to be more sensitive toward mapping lateral changes than the conventional amplitude-based texture approach, wherein seismic data is used as input. To evaluate the improvement, we applied the proposed workflow to the full-stack 3D seismic data from the Poseidon field, NW-shelf, Australia. This study demonstrates that a better demarcation of reservoir gas sands with improved lateral continuity is achievable with the presented approach compared to the conventional approach. In addition, we assess the implication of multi-stage faulting on facies distribution for effective reservoir characterization. This study also suggests a well-bounded potential reservoir facies distribution along the parallel fault lines. Thus, the proposed approach provides an efficient strategy by integrating the impedance information with texture attributes to improve the inference on reservoir heterogeneity, which can serve as a promising tool for identifying potential reservoir zones for both production benefits and fluid storage. [ABSTRACT FROM AUTHOR]

Metal ores are mineralogically characterised to understand their genesis in order to allow informed decisions on mineral processing and to recognise likely environmental risks upon mining. However, standard mineralogical techniques generate only two-dimensional information at best, which in addition may be subject to sampling and stereological errors. By contrast, computed tomography (CT) is a non-destructive imaging technique that allows three-dimensional analysis of solid materials. In the present study, two ore types of the Kara Fe-W deposit (Australia) were characterised using CT to examine their mineral texture and modal mineralogy as well as scheelite distribution and ore grade (WO3). The results show that scheelite is primarily associated with hydrous phases (e.g., epidote, chlorite, amphibole) and occurs as massive or disseminated mineral as well as vein-fill at minor and trace concentrations. This study demonstrates that CT of scheelite ore enables accurate 3D texture visualisation (volume, grain size distribution) and yields valid quantitative data on modal mineralogy and WO3 grade of individual ore samples. Consequently, CT analysis of scheelite-bearing ore provides information relevant for ore genesis studies and comminution strategies for the possible recovery of scheelite as a by-product from metalliferous ores. [ABSTRACT FROM AUTHOR]

Photovoltaic (PV) power prediction plays a significant role in supporting the stable operation and resource scheduling of integrated energy systems. However, the randomness and volatility of photovoltaic power generation will greatly affect the prediction accuracy. Focusing on this issue, a prediction framework is proposed in this research by developing an improved sparrow search algorithm (ISSA) to optimize the hyperparameters of long short-term memory (LSTM) neural networks. The ISSA is specially designed from the following three aspects to support a powerful search performance. Firstly, the initial population variety is enriched by using an enhanced sine chaotic mapping. Secondly, the relative position of neighboring producers is introduced to improve the producer position-updating strategy to enhance the global search capabilities. Then the Cauchy–Gaussian variation is utilized to help avoid the local optimal solution. Numerical experiments on 20 test functions indicate that ISSA could identify the optimal solution with better precision compared to SSA and PSO algorithms. Furthermore, a comparative study of PV power prediction methods is provided. The ISSA-LSTM algorithm developed in this paper and five benchmark models are implemented on a real dataset gathered from the Alice Springs area in Australia. In contrast to the SSA-LSTM model, most MAE, MAPE, and RMSE values of the proposed model are reduced by 20∼60%, demonstrating the superiority of the proposed model under various weather conditions and typical seasons. [ABSTRACT FROM AUTHOR]

The visual modeling method enables flexible interactions with rich graphical depictions of data and supports the exploration of the complexities of epidemiological analysis. However, most epidemiology visualizations do not support the combined analysis of objective factors that might influence the transmission situation, resulting in a lack of quantitative and qualitative evidence. To address this issue, we developed a portrait-based visual modeling method called +msRNAer. This method considers the spatiotemporal features of virus transmission patterns and multidimensional features of objective risk factors in communities, enabling portrait-based exploration and comparison in epidemiological analysis. We applied +msRNAer to aggregate COVID-19-related datasets in New South Wales, Australia, combining COVID-19 case number trends, geo-information, intervention events, and expert-supervised risk factors extracted from local government area-based censuses. We perfected the +msRNAer workflow with collaborative views and evaluated its feasibility, effectiveness, and usefulness through one user study and three subject-driven case studies. Positive feedback from experts indicates that +msRNAer provides a general understanding for analyzing comprehension that not only compares relationships between cases in time-varying and risk factors through portraits but also supports navigation in fundamental geographical, timeline, and other factor comparisons. By adopting interactions, experts discovered functional and practical implications for potential patterns of long-standing community factors regarding the vulnerability faced by the pandemic. Experts confirmed that +msRNAer is expected to deliver visual modeling benefits with spatiotemporal and multidimensional features in other epidemiological analysis scenarios. [ABSTRACT FROM AUTHOR]

The loss of marine biodiversity is a major global issue that needs to be prioritised. In Australia, a considerable proportion (48%) of its Exclusive Economic Zone is dedicated to marine protected areas. To effectively manage this network of marine protected areas Australia has recently introduced a Management Effectiveness system. This system is designed to identify, monitor, and manage natural values and the various activities and pressures affecting the Australian Marine Parks (AMPs). Key to this approach is the identification and accurate mapping of the location of these values and pressures acting on the seabed. The AusSeabed program is a national initiative in Australia aimed at improving access to bathymetric data and coordinating efforts to collect such data in Australian waters. This manuscript proposes a novel systematic processing method to create detailed and scalable geomorphometric maps from AusSeabed's bathymetric data holdings, intended as a standard operating procedure for initial bathymetric data interpretation in the AMPs. Utilising this workflow, we produce seafloor geomorphometry maps across 37 AMPs within which sufficient bathymetric data has been collected. These maps can be used 1) for predictive mapping of biological assemblages; 2) in field sampling design for the collection of 'ground truthing' data (e.g. underwater imagery and sediment samples) to validate habitat maps from bathymetric datasets; and 3) as input datasets for subsequent geomorphological mapping with a deeper understanding of seafloor processes. This research highlights the importance of robust geomorphometry classification standards to ensure consistency in mapping Australia's marine estate in preparation for the Decade of Oceans plans. The Seamap Australia program provides a stepwise approach to advancing Australia's national collection of bathymetric data into derived products that can enable habitat mapping of Australian waters, providing a foundational tool for the adaptive management of AMPs. [ABSTRACT FROM AUTHOR]

Plastics are now ubiquitous in the environment and have been studied in wildlife and in ecosystems for more than 50 years. Measurement of size, shape and colour data for individual fragments of plastic is labour‐intensive, unreliable and prone to observer bias, particularly when it comes to assessment of colour, which relies on arbitrary and inconsistently defined colour categorisations. There is a clear need for a standard method for data collection on plastic pollution, particularly one that can be readily automated given the number of samples involved.This study describes a new method for standardised photography of marine plastics in the 1–100 mm size range (meso‐ and macro‐plastics), including colour correction to account for any image‐to‐image variation in lighting that may impact colour reproduction or apparent brightness. Automated image analysis is then applied to detect individual fragments of plastic for quantitative measurement of size, shape, and colour.The method was tested on 3793 fragments of debris ingested by Flesh‐footed Shearwaters (Ardenna carneipes) on Lord Howe Island, Australia, and compare results from photos taken in two separate locations using different equipment. Photos were acquired of up to 250 fragments at a time with a spatial resolution of 70 μm/pixel and were colour‐corrected using a reference chart to ensure accurate reproduction of colour. The automated image analysis pipeline was found to have a 98% success rate at detecting fragments, and the different size and shape parameters that can be outputted by the pipeline were compared in terms of usefulness.The evidence shown in this study should strongly encourage the uptake of this method for cataloguing macro‐scale plastic pollution, as it provides substantially higher quality data with accurate, reliable measurements of size, shape and colour for individual plastics that can be readily compared between disparate datasets. [ABSTRACT FROM AUTHOR]

In recent years, mine site closure and rehabilitation have emerged as significant global challenges. The escalating number of abandoned mines, exemplified by over 60,000 in Australia in 2017, underscores the urgency. Growing public concerns and governmental focus on environmental issues are now jeopardising sustainable mining practices. This paper assesses the role of unmanned aerial vehicles (UAVs) in mine closure, exploring sensor technology, artificial intelligence (AI), and mixed reality (MR) applications. Prior research validates UAV efficacy in mining, introducing various deployable sensors. Some studies delve into AI's use for UAV data analysis, but a comprehensive review integrating AI algorithms with MR methods for mine rehabilitation is lacking. The paper discusses data acquisition methods, repeatability, and barriers toward fully autonomous monitoring systems for mine closure projects. While UAVs prove adaptable with various sensors, constraints such as battery life and payload capacity impact effectiveness. Although UAVs hold potential for AI testing in mine closure studies, these applications have been overlooked. AI algorithms are pivotal for creating autonomous systems, reducing operator intervention. Moreover, MR's significance in mine closure is evident, emphasising its application in the mining industry. Ultimately, a hybrid UAV–AI–MR technology is not only viable but essential for achieving successful mine closure and sustainable mining practices in the future. [ABSTRACT FROM AUTHOR]

We demonstrate that river discharge can be estimated by deriving water surface velocity estimates from satellite‐derived video imagery when combined with high‐resolution topography of channel geometry. Large Scale Particle Image Velocimetry (LSPIV) was used to map surface velocity from 28 s of 5 Hz satellite video acquired at a 1.2 m nominal ground spacing over the Darling River, Tilpa, Australia, during a 1‐in‐5‐year flood. We stabilized and assessed the uncertainty of the residual motion induced by the satellite platform, enhancing our sub‐pixel motion analysis, and quantified the sensitivity of image extraction rates on computed velocities. In the absence of in situ observations, LSPIV velocity estimates were validated against predictions from a calibrated 2D hydrodynamic model. Despite the confounding influence of selecting a surface velocity depth‐averaging coefficient, inference of discharge was within 0.3%–15% compared with gauging station measurements. These results provide a valuable foundation for refining satellite video LSPIV techniques. Plain Language Summary: Estimates of river flow are needed to manage water resources and flood risk. However, many of the world's rivers are not gauged, limiting hydrological understanding of river response to changing environmental conditions and storm events. We demonstrate that satellite video can be used to map velocity by tracking surface water features from one video frame to the next, and scaled to compute discharge where river geometry is known. Using a video of a flood on the River Tilpa, Australia, our results agree with ground‐based measurements to within 0.3%–15%. The ability to deploy satellites to acquire video anywhere globally could contribute to measuring discharge on ungauged rivers. Key Points: Satellite video acquired along 12.6 km of the River Darling, Australia, at 5 Hz for 28 s during a 1‐in‐5‐year storm eventSatellite video‐based velocities coupled with high resolution topography estimate riverine discharges to within 15% of in situ gauge dataParametrization of non‐contact velocimetry and choice of a depth‐averaging coefficient (α) influence the accuracy of discharge estimates [ABSTRACT FROM AUTHOR]

Environmental monitoring networks such as the Integrated Carbon Observation System (ICOS) in Europe, the National Ecological Observatory Network (NEON) in the U.S., or the Terrestrial Ecosystem Research Network (TERN) in Australia deploy different sampling schemes for in situ measurements. We report on the intercomparison of measurements of the canopy gap fraction with different digital hemispherical photography setups adopting ICOS, NEON, and TERN sampling schemes. The test was carried out at the Järvselja Radiation Transfer Model Intercomparison (RAMI) birch stand. Results show that spreading out sampling points which cover more of the plot is important for a good representation of the forest as a whole. The NEON tower plot layout scheme may be more prone to errors in overall canopy properties estimation than ICOS or TERN due to its compact sampling layout and should always be used in conjunction with its distributed plots. Different camera setups involving different camera operators, camera bodies, lenses and settings yield slightly varied results, and it is important to ensure that the images are taken in such a way that they would not be over or underexposed, or out of focus. As a conclusion we recommend always to carry out intercomparison measurements with old and new cameras when devices are upgraded. Our study contributes towards establishing the uncertainty and evaluating potential error budget stemming from collecting in situ measurements using different sampling schemes and camera setups. [ABSTRACT FROM AUTHOR]

The benefits of Hyperion hyperspectral data to agriculture have been studied at sites in the Coleambally Irrigation Area of Australia. Hyperion can provide effective measures of agricultural performance through the use of established spectral indexes if systematic and random noise is managed. The noise management strategy includes recognition of "bad" pixels, reducing the effects of vertical striping, and compensation for atmospheric effects in the data. It also aims to reduce compounding of these effects by image processing. As the noise structure is different for Hyperion's two spectrometers, noise reduction methods are best applied to each separately. Results show that a local destriping algorithm reduces striping noise without introducing unwanted effects in the image. They also show how data smoothing can clean the data and how careful selection of stable Hyperion bands can minimize residual atmospheric effects following atmospheric correction. Comparing hyperspectral indexes derived from Hyperion with the same indexes derived from ground-measured spectra allowed us to assess some of these impacts on the preprocessing options. It has been concluded that preprocessing, which includes fixing bad and outlier pixels, local destriping, atmospheric correction, and minimum noise fraction smoothing, provides improved results. If these or equivalent preprocessing steps are followed, it is feasible to develop a consistent and standardized time series of data that is compatible with field-scale and airborne measured indexes. Red-edge and leaf chlorophyll indexes based on the preprocessed data are shown to distinguish different levels of stress induced by water restrictions. [ABSTRACT FROM AUTHOR]

The exacerbation of wildfires, attributed to the effects of climate change, presents substantial risks to ecological systems, infrastructure, and human well-being. In the context of the Sustainable Development Goals (SDGs), particularly those related to climate action, prioritizing the assessment and management of the occurrence and intensity of extensive wildfires is of utmost importance. In recent times, there has been a significant increase in the frequency and severity of widespread wildfires worldwide, affecting several locations, including Australia, Italy, and the United States of America. The presence of complex phenomena marked by limited predictability leads to significant negative impacts on biodiversity and human lives. The utilization of satellite-derived data with neural networks, such as convolutional neural networks (CNNs), is a potentially advantageous approach for augmenting the monitoring capabilities of wildfires. This research examines the generalization capability of four neural network models, namely the fully connected (FC), one-dimensional (1D) CNN, two-dimensional (2D) CNN, and three-dimensional (3D) CNN model. Each model's performance, as measured by accuracy, recall, and F1 scores, is assessed through K-fold cross-validation. Subsequently, T-statistics and p-values are computed based on these metrics to conduct a statistical comparison among the different models, allowing us to quantify the degree of similarity or dissimilarity between them. By using training data from Australia and Sicily, the performances of the trained model are evaluated on the test dataset from Oregon. The results are promising, with cross-validation on the training dataset producing mean precision, recall, and F1 scores ranging between approximately 0.97 and 0.98. Especially, the fully connected model has superior generalization capabilities, whilst the 3D CNN offers more refined and less distorted classifications. However, certain issues, such as false fire detection and confusion between smoke and shadows, persist. The aforementioned methodologies offer significant perspectives on the capabilities of neural network technologies in supporting the detection and management of wildfires. These approaches address the crucial matter of domain transferability and the associated dependability of predictions in new regions. This study makes a valuable contribution to the ongoing efforts in climate change by assisting in monitoring and managing wildfires. [ABSTRACT FROM AUTHOR]

Coral reefs face an uncertain future punctuated by recurring climate-induced disturbances. Understanding how reefs can recover from and reassemble after mass bleaching events is therefore important to predict their responses and persistence in a rapidly changing ocean. On naturally extreme reefs characterized by strong daily temperature variability, coral heat tolerance can vary significantly over small spatial gradients but it remains poorly understood how this impacts bleaching resilience and recovery dynamics, despite their importance as resilience hotspots and potential refugia. In the macrotidal Kimberley region in NW Australia, the 2016 global mass bleaching event had a strong habitat-specific impact on intertidal and subtidal coral communities at our study site: corals in the thermally variable intertidal bleached less severely and recovered within six months, while 68% of corals in the moderately variable subtidal died. We therefore conducted benthic surveys 3.5 years after the bleaching event to determine potential changes in benthic cover and coral community composition. In the subtidal, we documented substantial increases in algal cover and live coral cover had not fully recovered to pre-bleaching levels. Furthermore, the subtidal coral community shifted from being dominated by branching Acropora corals with a competitive life history strategy to opportunistic, weedy Pocillopora corals which likely has implications for the functioning and stress resilience of this novel coral community. In contrast, no shifts in algal and live coral cover or coral community composition occurred in the intertidal. These findings demonstrate that differences in coral heat tolerance across small spatial scales can have large consequences for bleaching resilience and that spatial patchiness in recovery trajectories and community reassembly after bleaching might be a common feature on thermally variable reefs. Our findings further confirm that reefs adapted to high daily temperature variability play a key role as resilience hotspots under current climate conditions, but their ability to do so may be limited under intensifying ocean warming. [ABSTRACT FROM AUTHOR]

The use of objects by cetaceans is well known, and their ability to interact with their environment in complex behaviours has been demonstrated previously. However, baleen whales, including humpback whales (Megaptera novaeangliae), are less often observed to perform object use, but this behaviour might be more common than previously thought. Only a few isolated observations of interactions with seaweed have been reported in the scientific literature to date. The recovery of humpback whale populations, as well as the rise of technology such as unmanned aerial vehicles (UAVs) and the use of social media, allow for a new assessment of this object interaction. Here, we describe in detail three instances of "kelping" on the east coast of Australia derived from aerial observations. A summary of over 100 separate and unrelated events drawn from social media, documented by photographs and videos, suggests that this form of interaction with seaweed is observed across different populations. The form of interaction with seaweed is similar between regions, predominantly displayed between the rostrum and dorsal fin. This behaviour may be playful but could also serve additional benefits in the context of learning and socializing, as well as ectoparasite removal and skin treatment by utilizing brown algae's antibacterial properties. Establishing this type of behaviour as distributed across different populations is important to better understand the species' habitat preferences. [ABSTRACT FROM AUTHOR]

Interference competition has the potential to alter avian assemblages at long‐lasting arid zone waterholes, particularly in a warming world, as more potentially aggressive species frequent these sites to drink. We used camera traps and observational surveys to investigate interference competition between terrestrial avian species at six long‐lasting waterholes across three sampling seasons (two summers and one winter) within the MacDonnell Ranges Bioregion in central Australia. The proportion of individuals drinking for each of four dietary classes (granivores, nectarivores, omnivores, and insectivores) was modelled in relation to their abundance in the immediate waterhole habitat, which informed the potential for competition in each season. We then used the temporal overlap estimators to quantify the degree of competition between species at waterholes with species grouped into families (Meliphagidae, Ptilonorhynchidae, Estrildidae, and Rhipiduridae). We found the proportion of individuals drinking at waterholes was greatest during hot and dry periods, suggesting the potential for interference competition is greatest during these times. This was particularly the case for nectarivores where, in hot and dry conditions, the proportion of drinking individuals increased significantly as their abundance also increased in the waterhole habitat. We predicted that subordinate species would alter their activity periods to avoid competitive interactions with meliphagids (honeyeaters), however, we found there was a high degree of temporal overlap between all families sampled across all seasons. These results suggest subordinate species are unlikely to be excluded from long‐lasting waterholes by potentially aggressive species, such as honeyeaters. However, some species may face trade‐offs between foraging and accessing waterholes to stay hydrated as they shift their activity to avoid the hottest parts of the day during the summer months. Under global warming, extended hot and dry periods will likely create conditions where balancing energy and hydration requirements becomes increasingly difficult and results in the loss of body condition. [ABSTRACT FROM AUTHOR]

Pearl perch (Glaucosoma scapulare) are endemic to the east coast of Australia and have a long history of exploitation. Recent stock assessments indicate that the current rate of fishing mortality is unsustainable in the long term. To better inform the management of the pearl perch stock and to address gaps in our understanding of their reproductive biology, we investigated patterns in gonad development and estimated length- and age-at-maturity and batch fecundity from females collected from southern and central Queensland waters between 2018 and 2022. The mean gonadosomatic index (GSI) varied both temporally and spatially, with maxima in the austral autumn in southern Queensland and in summer in central Queensland, coinciding with sea surface temperatures between 25.26 and 26.32°C. The length- and age-at-maturity of females were 353 mm (fork length, FL) and 4.42 years, respectively, and batch fecundity (B) was correlated to FL such that Ln(B) = 2.45 × Ln(FL) + 3.90. Our results will inform a management strategy to recover the stock to acceptable levels of exploitation. [ABSTRACT FROM AUTHOR]

Environmental damage has been of much concern, particularly in coastal areas and the oceans, given climate change and the drastic effects of pollution and extreme climate events. Our present-day analytical capabilities, along with advancements in information acquisition techniques such as remote sensing, can be utilised for the management and study of coral reef ecosystems. In this paper, we present Reef-Insight, an unsupervised machine learning framework that features advanced clustering methods and remote sensing for reef habitat mapping. Our framework compares different clustering methods for reef habitat mapping using remote sensing data. We evaluate four major clustering approaches based on qualitative and visual assessments which include k-means, hierarchical clustering, Gaussian mixture model, and density-based clustering. We utilise remote sensing data featuring the One Tree Island reef in Australia's Southern Great Barrier Reef. Our results indicate that clustering methods using remote sensing data can well identify benthic and geomorphic clusters in reefs when compared with other studies. Our results indicate that Reef-Insight can generate detailed reef habitat maps outlining distinct reef habitats and has the potential to enable further insights for reef restoration projects. [ABSTRACT FROM AUTHOR]

With the development of the electric market, electric load forecasting has been increasingly pursued by many scholars. Because the electric load is affected by many factors, it is characterized by volatility and uncertainty, and it cannot be forecasted accurately only by a single model. In the research, a short‐term load forecasting integrated model is proposed to solve the problem of inaccurate forecasting of a single model. The key point of using the integrated model to forecast is to optimize the decomposed sequence to improve the accuracy of the forecast. empirical wavelet decomposition (EWT) is used to decompose the sequence into stationary sequences and avoid modal aliasing; the sparrow search algorithm (SSA) simulates the forecasting and anti‐forecasting behavior of the sparrow population, which is very similar to the electricity consumption behavior of various industries and has good optimization effect; generalized regression neural network (GRNN) is used for forecast and reconstruction; This is the EWT‐SSA‐GRNN model. This paper studies and analyzes the power load of a city in southern Australia. The results show that the integrated model reduces volatility through decomposition and optimization, and can improve forecast accuracy. [ABSTRACT FROM AUTHOR]

One of the most valuable and nutritionally essential agricultural commodities worldwide is milk. The European Union and New Zealand are the second- and third-largest exporting regions of milk products and rely heavily on pasture-based production systems. They are comparable to the Australian systems investigated in this study. With projections of herd decline, increased milk yield must be obtained from a combination of animal genetics and feed efficiencies. Accurate pasture biomass estimation across all seasons will improve feed efficiency and increase the productivity of dairy farms; however, the existing time-consuming and manual methods of pasture measurement limit improvements to utilisation. In this study, Sentinel-2 (S2) band and spectral index (SI) information were coupled with the broad season and management-derived datasets using a Random Forest (RF) machine learning (ML) framework to develop a perennial ryegrass (PRG) biomass prediction model accurate to +/−500 kg DM/ha, and that could predict pasture yield above 3000 kg DM/ha. Measurements of PRG biomass were taken from 11 working dairy farms across southeastern Australia over 2019–2021. Of the 68 possible variables investigated, multiple simulations identified 12 S2 bands and 9 SI, management and season as the most important variables, where Short-Wave Infrared (SWIR) bands were the most influential in predicting pasture biomass above 4000 kg DM/ha. Conditional Latin Hypercube Sampling (cLHS) was used to split the dataset into 80% and 20% for model calibration and internal validation in addition to an entirely independent validation dataset. The combined internal model validation showed R2 = 0.90, LCCC = 0.72, RMSE = 439.49 kg DM/ha, NRMSE = 15.08, and the combined independent validation had R2 = 0.88, LCCC = 0.68, RMSE = 457.05 kg DM/ha, NRMSE = 19.83. The key findings of this study indicated that the data obtained from the S2 bands and SI were appropriate for making accurate estimations of PRG biomass. Furthermore, including SWIR bands significantly improved the model. Finally, by utilising an RF ML model, a single 'global' model can automate PRG biomass prediction with high accuracy across extensive regions of all seasons and types of farm management. [ABSTRACT FROM AUTHOR]

Increased demand for sustainable timber products has resulted in large investments in agroforestry in Australia, with plantations growing various Pinus species, selected to suit a plantation's environment. Juvenile Pinus species have a low fire tolerance. With Australia's history of wildfires and the likelihood of climate change exacerbating that risk, the potential for a total loss of invested capital is high unless cost-effective targeted risk minimisation is part of forest management plans. Based on the belief that the understory profiles within the juvenile plantations are a major factor determining fuel hazard risks, an accurate assessment of these profiles is required to effectively mitigate those risks. At present, assessment protocols are largely reliant on ground-based observations, which are labour-intensive, time consuming, and expensive. This research project investigates the effectiveness of using geospatial analysis of drone-derived photographic data collected in the commercial pine plantations of south-eastern Queensland as a cost-saving alternative to current fuel hazard risk assessment practices. Understory composition was determined using the supervised classification of orthomosaic images together with derivations of canopy height models (CHMs). The CHMs were subjected to marker-controlled watershed segmentation (MCWS) analysis, isolating and removing the plantation pine trees, enabling the quantification of understory fuel profiles. The method used proved highly applicable to immature forest environments with minimal canopy closure, but became less reliable for close canopied older plantations. [ABSTRACT FROM AUTHOR]

Coral reefs and their associated marine communities are increasingly threatened by anthropogenic climate change. A key step in the management of climate threats is an efficient and accurate end-to-end system of coral monitoring that can be generally applied to shallow water reefs. Here, we used RGB drone-based imagery and a deep learning algorithm to develop a system of classifying bleached and unbleached corals. Imagery was collected five times across one year, between November 2018 and November 2019, to assess coral bleaching and potential recovery around Lord Howe Island, Australia, using object-based image analysis. This training mask was used to develop a large training dataset, and an mRES-uNet architecture was chosen for automated segmentation. Unbleached coral classifications achieved a precision of 0.96, a recall of 0.92, and a Jaccard index of 0.89, while bleached corals achieved 0.28 precision, 0.58 recall, and a 0.23 Jaccard index score. Subsequently, methods were further refined by creating bleached coral objects (>16 pixels total) using the neural network classifications of bleached coral pixels, to minimize pixel error and count bleached coral colonies. This method achieved a prediction precision of 0.76 in imagery regions with >2000 bleached corals present, and 0.58 when run on an entire orthomosaic image. Bleached corals accounted for the largest percentage of the study area in September 2019 (6.98%), and were also significantly present in March (2.21%). Unbleached corals were the least dominant in March (28.24%), but generally accounted for ~50% of imagery across other months. Overall, we demonstrate that drone-based RGB imagery, combined with artificial intelligence, is an effective method of coral reef monitoring, providing accurate and high-resolution information on shallow reef environments in a cost-effective manner. [ABSTRACT FROM AUTHOR]

Not all fossil sites preserve microfossils that can be extracted using acid digestion, which may leave knowledge gaps regarding a site's age or environmental characteristics. Here we report on a citizen science approach that was developed to identify microfossils in situ on the surface of sedimentary rocks. Samples were collected from McGraths Flat, a recently discovered Miocene rainforest lake deposit located in central New South Wales, Australia. Composed entirely of iron-oxyhydroxide, McGraths Flat rocks cannot be processed using typical microfossil extraction protocols e.g., acid digestion. Instead, scanning electron microscopy (SEM) was used to automatically acquire 25,200 high-resolution images from the surface of three McGraths Flat samples, covering a total area of 1.85 cm2. The images were published on the citizen science portal DigiVol, through which 271 citizen scientists helped to identify 300 pollen and spores. The microfossil information gained in this study is biostratigraphically relevant and can be used to constrain the environmental characteristics of McGraths Flat. Our findings suggest that automated image acquisition coupled with an evaluation by citizen scientists is an effective method of determining the age and environmental characteristics of fossiliferous rocks that cannot be investigated using traditional methods such as acid digestion. [ABSTRACT FROM AUTHOR]

Satellite imagery is the only feasible approach to annual monitoring and reporting on land cover change. Unfortunately, conventional pixel-based classification methods based on spectral response only (e.g., using random forests algorithms) have shown a lack of spatial and temporal stability due, for instance, to variability between individual pixels and changes in vegetation condition, respectively. Machine learning methods that consider spatial patterns in addition to reflectance can address some of these issues. In this study, a convolutional neural network (CNN) model, U-Net, was trained for a 500 km × 500 km region in southeast Australia using annual Landsat geomedian data for the relatively dry and wet years of 2018 and 2020, respectively. The label data for model training was an eight-class classification inferred from a static land-use map, enhanced using forest-extent mapping. Here, we wished to analyse the benefits of CNN-based land cover mapping and reporting over 34 years (1987–2020). We used the trained model to generate annual land cover maps for a 100 km × 100 km tile near the Australian Capital Territory. We developed innovative diagnostic methods to assess spatial and temporal stability, analysed how the CNN method differs from pixel-based mapping and compared it with two reference land cover products available for some years. Our U-Net CNN results showed better spatial and temporal stability with, respectively, overall accuracy of 89% verses 82% for reference pixel-based mapping, and 76% of pixels unchanged over 33 years. This gave a clearer insight into where and when land cover change occurred compared to reference mapping, where only 30% of pixels were conserved. Remaining issues include edge effects associated with the CNN method and a limited ability to distinguish some land cover types (e.g., broadacre crops vs. pasture). We conclude that the CNN model was better for understanding broad-scale land cover change, use in environmental accounting and natural resource management, whereas pixel-based approaches sometimes more accurately represented small-scale changes in land cover. [ABSTRACT FROM AUTHOR]

Symbiotic associations between zoantharians and sponges can be divided into two groups: those that associate with Demospongiae and those that associate with Hexactinellida. Parachurabana shinseimaruae Kise, gen. nov. et sp. nov., a new genus and a new species of Hexactinellida-associated zoantharian from Japanese waters, is described. It is characterized by a combination of the following: i) its host hexactinellid sponge, ii) very flat polyps, iii) cteniform endodermal marginal muscles, and iv) characteristic mutations in three mitochondrial regions (including a unique 26-bp deletion in 16S ribosomal DNA) and three nuclear regions. Parachurabana shinseimaruae Kise, gen. nov. et sp. nov. is the third genus in the family Parazoanthidae that is reported to be associated with Hexasterophora sponges. Although specimens have so far only been collected on Takuyo-Daigo Seamount off Minami-Torishima Island in Japan, unidentified zoantharians of similar description have been reported from the waters around Australia, indicating that the species might be widespread across the Pacific. [ABSTRACT FROM AUTHOR]

In Australia, peanuts are mainly grown in Queensland with tropical and subtropical climates. The most common foliar disease that poses a severe threat to quality peanut production is late leaf spot (LLS). Unmanned aerial vehicles (UAVs) have been widely investigated for various plant trait estimations. The existing works on UAV-based remote sensing have achieved promising results for crop disease estimation using a mean or a threshold value to represent the plot-level image data, but these methods might be insufficient to capture the distribution of pixels within a plot. This study proposes two new methods, namely measurement index (MI) and coefficient of variation (CV), for LLS disease estimation on peanuts. We first investigated the relationship between the UAV-based multispectral vegetation indices (VIs) and the LLS disease scores at the late growth stages of peanuts. We then compared the performances of the proposed MI and CV-based methods with the threshold and mean-based methods for LLS disease estimation. The results showed that the MI-based method achieved the highest coefficient of determination and the lowest error for five of the six chosen VIs whereas the CV-based method performed the best for simple ratio (SR) index among the four methods. By considering the strengths and weaknesses of each method, we finally proposed a cooperative scheme based on the MI, the CV and the mean-based methods for automatic disease estimation, demonstrated by applying this scheme to the LLS estimation in peanuts. [ABSTRACT FROM AUTHOR]

Simple Summary: Cattle are vulnerable to hot environmental temperatures, and this can lead to severe heat stress, resulting in behaviour and welfare issues. The automated recording of cattle behavioural responses would be helpful in the timely diagnosis of cattle experiencing heat loading. We investigated whether video-digitised image analysis could identify behavioural responses of cattle, especially during heat stress conditions. It was further explored whether a substituted diet (in which some of the grain normally fed as a finisher diet was substituted for forage) would affect the behavioural responses to heat stress, which were measured by digitised movements. An increased digitally recorded movement in animals was observed during high environmental temperatures, which was related to stepping and grooming/scratching activities in standing animals. Under hot temperatures, cattle on the substituted diet displayed less discomfort in terms of a smaller increase in digitally recorded movements than those on the finisher diet. The results suggest that automated video digitisation software could be used as a non-invasive tool for tracking cattle behavioural responses during hot conditions and may have broader applications for behavioural studies. Cattle change their behaviour in response to hot temperatures, including by engaging in stepping that indicates agitation. The automated recording of these responses would be helpful in the timely diagnosis of animals experiencing heat loading. Behavioural responses of beef cattle to hot environmental conditions were studied to investigate whether it was possible to assess behavioural responses by video-digitised image analysis. Open-source automated behavioural quantification software was used to record pixel changes in 13 beef cattle videorecorded in a climate-controlled chamber during exposure to a simulated typical heat event in Queensland, Australia. Increased digitised movement was observed during the heat event, which was related to stepping and grooming/scratching activities in standing animals. The 13 cattle were exposed in two cohorts, in which the first group of cattle (n = 6) was fed a standard finisher diet based on a high percentage of cereal grains, and the second group of cattle (n = 7) received a substituted diet in which 8% of the grains were replaced by lucerne hay. The second group displayed a smaller increase in digitised movements on exposure to heat than the first, suggesting less discomfort under hot conditions. The results suggest that cattle exposed to heat display increased movement that can be detected automatically by video digitisation software, and that replacing some cereal grain with forage in the diet of feedlot cattle may reduce the measured activity responses to the heat. [ABSTRACT FROM AUTHOR]

Numerous hectares of land are destroyed by wildfires every year, causing harm to the environment, the economy, and the ecology. More than fifty million acres have burned in several states as a result of recent forest fires in the Western United States and Australia. According to scientific predictions, as the climate warms and dries, wildfires will become more intense and frequent, as well as more dangerous. These unavoidable catastrophes emphasize how important early wildfire detection and prevention are. The energy management system described in this paper uses an unmanned aircraft system (UAS) with air quality sensors (AQSs) to monitor spot fires before they spread. The goal was to develop an efficient autonomous patrolling system that detects early wildfires while maximizing the battery life of the UAS to cover broad areas. The UAS will send real-time data (sensor readings, thermal imaging, etc.) to a nearby base station (BS) when a wildfire is discovered. An optimization model was developed to minimize the total amount of energy used by the UAS while maintaining the required levels of data quality. Finally, the simulations showed the performance of the proposed solution under different stability conditions and for different minimum data rate types. [ABSTRACT FROM AUTHOR]

This study evaluates the ability of a random forest classifier to identify tropical cyclone (TC) intensification or weakening prior to landfall over the western region of the Southwest Pacific Ocean (SWPO) basin. For both Australia mainland and SWPO island cases, when a TC first crosses land after spending ≥24 h over the ocean, the closest hour prior to the intersection is considered as the landfall hour. If the maximum wind speed (Vmax) at the landfall hour increased or remained the same from the 24-h mark prior to landfall, the TC is labeled as intensifying and if the Vmax at the landfall hour decreases, the TC is labeled as weakening. Geophysical and aerosol variables closest to the 24 h before landfall hour were collected for each sample. The random forest model with leave-one-out cross validation and the random oversampling example technique was identified as the best-performing classifier for both mainland and island cases. The model identified longitude, initial intensity, and sea skin temperature as the most important variables for the mainland and island landfall classification decisions. Incorrectly classified cases from the test data were analyzed by sorting the cases by their initial intensity hour, landfall hour, monthly distribution, and 24-h intensity changes. TC intensity changes near land strongly impact coastal preparations such as wind damage and flood damage mitigations; hence, this study will contribute to improve identifying and prioritizing prediction of important variables contributing to TC intensity change before landfall. [ABSTRACT FROM AUTHOR]

The visual modeling method enables flexible interactions with rich graphical depictions of data and supports the exploration of the complexities of epidemiological analysis. However, most epidemiology visualizations do not support the combined analysis of objective factors that might influence the transmission situation, resulting in a lack of quantitative and qualitative evidence. To address this issue, we developed a portrait-based visual modeling method called +msRNAer. This method considers the spatiotemporal features of virus transmission patterns and multidimensional features of objective risk factors in communities, enabling portrait-based exploration and comparison in epidemiological analysis. We applied +msRNAer to aggregate COVID-19-related datasets in New South Wales, Australia, combining COVID-19 case number trends, geo-information, intervention events, and expert-supervised risk factors extracted from local government area-based censuses. We perfected the +msRNAer workflow with collaborative views and evaluated its feasibility, effectiveness, and usefulness through one user study and three subject-driven case studies. Positive feedback from experts indicates that +msRNAer provides a general understanding for analyzing comprehension that not only compares relationships between cases in time-varying and risk factors through portraits but also supports navigation in fundamental geographical, timeline, and other factor comparisons. By adopting interactions, experts discovered functional and practical implications for potential patterns of long-standing community factors regarding the vulnerability faced by the pandemic. Experts confirmed that +msRNAer is expected to deliver visual modeling benefits with spatiotemporal and multidimensional features in other epidemiological analysis scenarios. [ABSTRACT FROM AUTHOR]

Due to their (a) lower draught force requirements and (b) ability to work at deeper operation depths and faster operation speeds, disc ploughs have gained interest in Australia. A modified version of the disc plough that involves removing every second disc and fitting larger and often more concave discs has become popular. However, the development of the one-way modified disc plough is in its infancy, and a detailed analysis is required, particularly on soil movement. Historically, the soil movement analysis of the soil–tool interactions is conducted using empirical methods. However, the experimental tests are resource and labour intensive. When the soil and tool interaction can be accurately modelled, more efficient tools can be designed without performing expensive field tests, which may only be undertaken at certain times of the year. This study modelled the interaction between soil and a one-way modified disc plough using the discrete element method (DEM). As the disc plough is a passive-driven tool, the rotational speed of the disc plough was modelled using DEM-MBD (multi-body dynamic) coupling. The results of the study show that DEM-MBD coupling can predict the rotational speed of the disc plough with a maximum relative error of 6.9%, and a good correlation was obtained between the DEM-predicted and actual soil movement (R2 = 0.68). [ABSTRACT FROM AUTHOR]

Summary: Landscape rehabilitation following mining is required to be resilient to disturbance impacts such as fire, drought and disease. As mining companies undergo the process of rehabilitation certification and mine closure, there are notable knowledge gaps on the ecological risks associated with mature rehabilitated landscapes, based largely on the assumption that rehabilitation is analogous to reference communities. However, the response to fire disturbance across a range of landscapes remains largely untested and in particular there is limited understanding of recovery traits of plant species that occur naturally or are commonly seeded into rehabilitation. In August 2018, a controlled fire was applied to 37 hectares of 12‐year‐old coal‐mine rehabilitation in central Queensland, Australia. We used a combination of (i) ground plot surveys and (ii) drone imagery to compare the vegetation response of burnt woody species to unburnt controls prior to, and for, two years following the fire. The survival of the most dominant shrub species found on the rehabilitation site was significantly impacted by the fire. Old Man Saltbush (Atriplex nummularia Lindl. subsp. nummularia) recorded significant post‐fire mortality, with ground surveys recording an average reduction of 89% of stems per hectare across the burnt site, while unburnt controls remained unchanged. The plot data analysis was supported with high spatial and temporal resolution drone imagery, classified using a Random Forest machine‐learning approach. Change analysis of these maps showed a significant decline of 82% in Old Man Saltbush plant density and 92% reduction in foliage cover following the fire. In addition, the mean canopy area of individual Old Man Saltbush shrubs reduced significantly from a pre‐fire mean of 11.3 to 4.8 m2 two years following the fire. A spatial proximity analysis showed that those individuals that survived the fire were located significantly closer to unburnt areas and bare spoil, indicating that discontinuous ground fuel loads can greatly improve the survivability of individuals. This study provides new evidence on the contested fire sensitivity of Old Man Salt bush and demonstrates the risk that future climate‐driven extreme events may have on the resilience of novel ecosystems. [ABSTRACT FROM AUTHOR]

Forecast of tree fruit yield requires prediction of harvest time fruit size as well as fruit number. Mango (Mangifera indica L.) fruit mass can be estimated from correlation to measurements of fruit length (L), width (W) and thickness (T). On-tree measurements of individually tagged fruit were undertaken using callipers at weekly intervals until the fruit were past commercial maturity, as judged using growing degree days (GDD), for mango cultivars 'Honey Gold', 'Calypso' and 'Keitt' at four locations in Australia and Brazil during the 2020/21 and 21/22 production seasons. Across all cultivars, the linear correlation of fruit mass to LWT was characterized by a R2 of 0.99, RMSE of 29.9 g and slope of 0.5472 g/cm3, while the linear correlation of fruit mass to L ( (W + T) 2 )2, mimicking what can be measured by machine vision of fruit on tree, was characterized by a R2 of 0.97, RMSE of 25.0 g and slope of 0.5439 g/cm3. A procedure was established for the prediction of fruit size at harvest based on measurements made five and four or four and three weeks prior to harvest (approx. 514 and 422 GDD, before harvest, respectively). Linear regression models on weekly increase in fruit mass estimated from lineal measurements were characterized by an R2 > 0.88 for all populations, with an average slope (rate of increase) of 19.6 ± 7.1 g/week, depending on cultivar, season and site. The mean absolute percentage error for predicted mass compared to harvested fruit weight for estimates based on measurements of the earlier and later intervals was 16.3 ± 1.3% and 4.5 ± 2.4%, respectively. Measurement at the later interval allowed better accuracy on prediction of fruit tray size distribution. A recommendation was made for forecast of fruit mass at harvest based on in-field measurements at approximately 400 to 450 GDD units before harvest GDD and one week later. [ABSTRACT FROM AUTHOR]

The use of drones and remote sensing in combination with geospatial analysis is a cost-efficient way to monitor energy distribution networks, especially those in fire-prone areas. This study investigated the use of image and photogrammetric analysis together with segmentation algorithms to assess vegetation height and volume in power line corridors in Southeast Queensland, Australia. Various fuel reduction techniques, including mega-mulching, spot sprays and cool mosaic burns, were implemented, and drone-generated models were employed to evaluate their effectiveness. The fuel hazard reduction and regrowth in terms of vegetation height and volume were recorded and analysed. Importantly, the study demonstrates a robust correlation (R2 = 0.9073; df = 1,16; F = 156; p <.001) between field observations and drone-derived models, affirming the efficacy of this method in assessing fuel heights. This validation suggests that the approach could represent a viable, cost-efficient option for future monitoring and management of energy distribution networks in fire-prone areas. [ABSTRACT FROM AUTHOR]