Today’s global coral bleaching events are the worst kind of climate warning.

Coral reefs support numerous marine organisms and are an important source of coastal protection from storms and floods, representing a major part of marine ecosystems. However coral reefs face increasing threats from pollution, ocean acidification, and sea temperature anomalies, making efficient protection and monitoring heavily urgent. Therefore, this study presents a novel machine-learning-based coral bleaching classification system based on a diverse global dataset with samples of healthy and bleached corals under varying environmental conditions, including deep seas, marshes, and coastal zones. We benchmarked and compared three state-of-the-art models: Residual Neural Network (ResNet), Vision Transformer (ViT), and Convolutional Neural Network (CNN). After comprehensive hyperparameter tuning, the CNN model achieved the highest accuracy of 88%, outperforming existing benchmarks. Our findings offer important insights into autonomous coral monitoring and present a comprehensive analysis of the most widely used computer vision models.

Coral reefs are on the brink of collapse, with climate change, ocean acidification, and pollution leading to a projected 70-90% loss of coral species within the next decade. Restoration efforts are crucial, but their success hinges on introducing automation to upscale efforts. We present automated deployment of coral re-seeding devices powered by artificial intelligence, computer vision, and robotics. Specifically, we perform automated substrate classification, enabling detection of areas of the seafloor suitable for coral growth, thus significantly reducing reliance on human experts and increasing the range and efficiency of restoration. Real-world testing of the algorithms on the Great Barrier Reef leads to deployment accuracy of 77.8%, sub-image patch classification of 89.1%, and real-time model inference at 5.5 frames per second. Further, we present and publicly contribute a large collection of annotated substrate image data to foster future research in this area.

Nitrate ($\text{NO}_3^-$) is a form of dissolved inorganic nitrogen derived primarily from anthropogenic sources. The recent increase in river-discharged nitrate poses a major risk for coral bleaching in the Great Barrier Reef (GBR) lagoon. Although nitrate is an optically inactive (i.e., colourless) constituent, previous studies have demonstrated there is an indirect, non-causal relationship between water surface nitrate and water-leaving reflectance that is mediated through optically active water quality parameters such as total suspended solids and coloured dissolved organic matter. This work aims to advance our understanding of this relationship with an effort to measure time-series nitrate and simultaneous hyperspectral reflectance at the Fitzroy River estuary, Queensland, Australia. Time-series observations revealed periodic cycles in nitrate loads due to the tidal influence in the estuarine study site. The water surface nitrate loads were predicted from hyperspectral reflectance and water salinity measurements, with hyperspectral reflectance indicating the concentrations of optically active variables and salinity indicating the mixing of river water and seawater proportions. The accuracy assessment of model-predicted nitrate against in-situ measured nitrate values showed that the predicted nitrate values correlated well with the ground-truth data, with an $R^2$ score of 0.86, and an RMSE of 0.03 mg/L. This work demonstrates the feasibility of predicting water surface nitrate from hyperspectral reflectance and salinity measurements.

Predicting Sea Surface Temperature (SST) in the Great Barrier Reef (GBR) region is crucial for the effective management of its fragile ecosystems. This study provides a rigorous comparative analysis of several machine learning techniques to identify the most effective method for SST prediction in this area. We evaluate the performance of ridge regression, Least Absolute Shrinkage and Selection Operator (LASSO), Random Forest, and Extreme Gradient Boosting (XGBoost) algorithms. Our results reveal that while LASSO and ridge regression perform well, Random Forest and XGBoost significantly outperform them in terms of predictive accuracy, as evidenced by lower Mean Squared Error (MSE), Mean Absolute Error (MAE), and Root Mean Squared Prediction Error (RMSPE). Additionally, XGBoost demonstrated superior performance in minimizing Kullback- Leibler Divergence (KLD), indicating a closer alignment of predicted probability distributions with actual observations. These findings highlight the efficacy of using ensemble methods, particularly XGBoost, for predicting sea surface temperatures, making them valuable tools for climatological and environmental modeling.

Growing up in Rhode Island (the Ocean State), I lived very close to the water. Over the years, I have seen the effects of sea level rise and rapid erosion. Entire houses and beaches have slowly been consumed by the tide. I have witnessed first hand how climate change is rapidly changing the ocean ecosystem. Sometimes I feel overwhelmed by the inexorability of climate change.

Marine biologists have a new AI tool for monitoring and protecting coral reefs. The project--a collaboration between Google and Australia's Commonwealth Scientific and Industrial Research Organization (CSIRO)--employs computer vision detection models to pinpoint damaging outbreaks of crown-of-thorns starfish (COTS) through a live camera feed. Keeping a closer eye on reefs helps scientists address growing populations quickly, to protect the valuable Great Barrier Reef ecosystem. Despite covering less than 1% of the vast ocean floor, coral reefs support about 25% of sea species including fish, invertebrates, and marine mammals. When healthy, these productive marine environments provide commercial and subsistence fishing and income for tourism and recreational businesses.

Seagrass meadows are a key ecosystem of the Great Barrier Reef World Heritage Area, providing one of the natural heritage attributes underpinning the reef’s outstanding universal value. We reviewed approaches employed to date to create maps of seagrass meadows in the optically complex waters of the Great Barrier Reef and explored enhanced mapping approaches with a focus on emerging technologies, and key considerations for future mapping. Our review showed that field-based mapping of seagrass has traditionally been the most common approach in the GBRWHA, with few attempts to adopt remote sensing approaches and emerging technologies. Using a series of case studies to harness the power of machine- and deep-learning, we mapped seagrass cover with PlanetScope and UAV-captured imagery in a variety of settings. Using a machine-learning pixel-based classification coupled with a bootstrapping process, we were able to significantly improve maps of seagrass, particularly in low cover, fragmented and complex habitats. We also used deep-learning models to derive enhanced maps from UAV imagery. Combined, these lessons and emerging technologies show that more accurate and efficient seagrass mapping approaches are possible, producing maps of higher confidence for users and enabling the upscaling of seagrass mapping into the future.

The most comprehensive deep-sea study of two marine parks off Australia has given a fascinating glimpse into what lives there. Scientists have told the BBC how they used an underwater robot to make a host of discoveries.

Coral reefs are an essential element in our global ecosystem, offering shelter to a quarter of marine life and providing a food source, income, and coastal buffer to over 500 million people across the globe. Yet because of rising ocean temperatures, which results in coral bleaching (check out TechRepublic's coverage of how tech is helping protect the Great Barrier Reef) as well as overfishing and reckless coastal development, coral reefs are endangered: Half of the Great Barrier Reef is dead. Today, to celebrate the 50th annual Earth Day, Intel, Accenture, and the Sulubaaï Environmental Foundation (SEF) present Project: CORaiL. The joint initiative will use the power of artificial intelligence (AI) "to monitor, recreate, and restore coral reefs," according to the release. To gauge the reef health, Project: CORail calculated the number and type of fish in a reef.