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Corals are sessile invertebrates living underwater in colorful structures known as reefs. Unfortunately, coral's temperature sensitivity is causing color bleaching, which hosts organisms that are crucial and consequently affect marine pharmacognosy. To address this problem, many researchers are developing cures and treatment procedures to restore bleached corals. However, before the cure, the researchers need to precisely localize the bleached corals in the Great Barrier Reef. The researchers have developed various visual classification frameworks to localize bleached corals. However, the performance of those techniques degrades with variations in illumination, orientation, scale, and view angle. In this paper, we develop highly noise-robust and invariant robust localization using bag-of-hybrid visual features (RL-BoHVF) for bleached corals by employing the AlexNet DNN and ColorTexture handcrafted by raw features. It is observed that the overall dimension is reduced by using the bag-of-feature method while achieving a classification accuracy of 96.20% on the balanced dataset collected from the Great Barrier Reef of Australia. Furthermore, the localization performance of the proposed model was evaluated on 342 images, which include both train and test segments. The model achieved superior performance compared to other standalone and hybrid DNN and handcrafted models reported in the literature. [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]

• River plumes are a source of pollutants in the Great Barrier Reef, Australia. • Plume dispersion is simulated using a coupled Lagrangian-Eulerian-ocean model. • Lagrangian particle density is computed from application of Voronoi diagrams. • Lagrangian particle densities and Eulerian tracer concentrations are comparable. • Lagrangian model is validated using satellite imagery and satellite Chlorophyll-a. In this paper, we describe methods to verify the adequacy and accuracy of Lagrangian particles from a Lagrangian model to reproduce the concentrations of a passive tracer from an Eulerian-model in river plumes. The modelling simulates plumes from two major rivers discharging in the Great Barrier Reef (GBR), Australia, under real-world scenarios. The study has been a part of a major project to aid in the protection of the GBR system from the impacts of extreme events and climate change. We employ the Regional Ocean Modelling System (ROMS) activated with its built-in Lagrangian model, and forced with wind fields from global models and recorded river volume discharges. The ROMS-Lagrangian model tracks the Lagrangian particles using the spatially interpolated velocities computed on the Eulerian ROMS three-dimensional (3D) grid. The Lagrangian particles are released in the river in proportion to the measured river volume flux. We apply a novel technique that exploits Voronoi polygon areas to convert Lagrangian particle separation into a concentration field. This facilitates comparison with the passive tracer concentrations driven by the Eulerian velocities computed on the ROMS 3D grid. We evaluate the fate of Lagrangian particles activated on a 4-km grid resolution with those of equivalent Eulerian tracer concentrations. For validation, we compare the Lagrangian particles with the Eulerian passive tracer simulated using a higher model grid resolution of 500-m, and found that the computed Lagrangian particle concentrations showed similar overall patterns compared to the passive tracer concentrations from both the coarse 4-km, and high-resolution 500-m Eulerian models. The spatial extent of the particles was in better agreement with the coarse 4-km model than with the higher resolution 500-m model. The clustering of particles resulted in structures at finer scales than the Eulerian model, over the high particle density areas that compared well against observations. The Lagrangian particles are able to capture the general river plume patterns seen in the satellite MODIS images and the satellite derived Chlorophyll-a. We further compare the online ROMS model with an offline particle tracker, OceanPARCELS and determined the similarity of results from both online and offline methods. Overall, our study demonstrates the viability to use Lagrangian particles to reproduce the Eulerian tracer properties, which can be combined with particle properties include tracer age providing to enhance the guidance on the distribution and concentration of effluent from localised flooding river events. [ABSTRACT FROM AUTHOR]

Aerosols deposited into the Great Barrier Reef (GBR) contain iron (Fe) and other trace metals, which may act as micronutrients or as toxins to this sensitive marine ecosystem. In this paper, we quantified the atmospheric deposition of Fe and investigated aerosol sources in Mission Beach (Queensland) next to the GBR. Leaching experiments were applied to distinguish pools of Fe with regard to its solubility. The labile Fe concentration in aerosols was 2.3–10.6 ng m−3, which is equivalent to 4.9%–11.4% of total Fe and was linked to combustion and biomass burning processes, while total Fe was dominated by crustal sources. A one-day precipitation event provided more soluble iron than the average dry deposition flux, 0.165 and 0.143 μmol m−2 day−1, respectively. Scanning Electron Microscopy indicated that alumina-silicates were the main carriers of total Fe and samples affected by combustion emissions were accompanied by regular round-shaped carbonaceous particulates. Collected aerosols contained significant amounts of Cd, Co, Cu, Mo, Mn, Pb, V, and Zn, which were mostly (47.5%–96.7%) in the labile form. In this study, we provide the first field data on the atmospheric delivery of Fe and other trace metals to the GBR and propose that this is an important delivery mechanism to this region. [ABSTRACT FROM AUTHOR]

Estimating the impact of environmental processes on vertical reef development in geological time is a very challenging task. pyReef-Core is a deterministic carbonate stratigraphic forward model designed to simulate the key biological and environmental processes that determine vertical reef accretion and assemblage changes in fossil reef drill cores. We present a Bayesian framework called Bayesreef for the estimation and uncertainty quantification of parameters in pyReef-Core that represent environmental conditions affecting the growth of coral assemblages in geological timescales. Weencounter multimodal posterior distributions and investigate the challenges of sampling using Markov chain Monte-Carlo (MCMC) methods, which includes parallel tempering MCMC. We use a synthetic reef-core to investigate fundamental issues and then apply the methodology to a selected reef-core from the Great Barrier Reef in Australia. The results show that Bayesreef accurately estimates and provides uncertainty quantification of the selected parameters that represent environment and ecological conditions in pyReef-Core. Bayesreef provides insights into the complex posterior distributions of the parameters in pyReef-Core , which provides the groundwork for future research in this area. • Estimating the impact of environmental processes on vertical reef development in geological time is a very challenging task. • pyReef-Core is a deterministic carbonate stratigraphic forward model designed to simulate the key biological and environmental processes. • This paper presents Bayesreef for the estimation and uncertainty quantification of parameters in pyReef-Core. • The results show that Bayesreef accurately estimates and provides uncertainty quantification for parameters in pyReef-Core. • Bayesreef provides insights in of pyReef-Core and establishes the groundwork for future research. [ABSTRACT FROM AUTHOR]