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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]

Achieving coastal and shallow-water bathymetry is essential for understanding the marine environment and for coastal management. Bathymetric data in shallow sea areas can currently be obtained using SDB (satellite-derived bathymetry) with multispectral satellites based on depth inversion models. In situ bathymetric data are crucial for validating empirical models but are currently limited in remote and unapproachable areas. In this paper, instead of using the measured water depth data, ICESat-2 (Ice, Cloud, and Land Elevation Satellite-2) ATL03 bathymetric points at different acquisition dates and multispectral imagery from Sentinel-2/GeoEye-1 were used to train and evaluate water depth inversion empirical models in two study regions: Shanhu Island in the South China Sea, and Heron Island in the Great Barrier Reef (GBR) in Australia. However, different sediment types also influenced the SDB results. Therefore, three types of sediments (sand, reef, and coral/algae) were analyzed for Heron Island, and four types of sediments (sand, reef, rubble and coral/algae) were analyzed for Shanhu Island. The results show that accuracy generally improved when sediment classification information was considered in both study areas. For Heron Island, the sand sediments showed the best performance in both models compared to the other sediments, with mean R2 and RMSE values of 0.90 and 1.52 m, respectively, representing a 5.6% improvement of the latter metric. For Shanhu Island, the rubble sediments showed the best accuracy in both models, and the average R2 and RMSE values were 0.97 and 0.65 m, respectively, indicating an RMSE improvement of 15.5%. Finally, bathymetric maps were generated in two regions based on the sediment classification results. [ABSTRACT FROM AUTHOR]

Wireless Sensor Networks promised to do for observation systems what consumer electronics have done for areas like photography-drive down the price per observation (photograph), introduce new functionality and capabilities, and make, what had been a relatively exclusive set of technologies and capabilities, ubiquitous. While this may have been true for some terrestrial sensor networks there are issues in the marine environment that have limited the realization of ubiquitous cheap sensing. This paper reports on the lessons learned from two years of operation of wireless sensor networks deployed at seven coral reefs along the Great Barrier Reef in north-eastern Australia. [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]