Williams et al. Size of Coral Reef VME testing in different environments. Importantly, these results should give confidence for stakeholder uptake and form the basis for better predictive VME models at larger spatial scales and beyond single taxa.

A multiscale maximum entropy method (MEM) for image deconvolution is implemented and applied to MODIS (moderate resolution imaging spectroradiometer) data to remove instrument point-spread function (PSF) effects. The implementation utilizes three efficient computational methods: a fast Fourier transform convolution, a wavelet image decomposition and an algorithm for gradient method step-size estimation that together enable rapid image deconvolution. Multiscale entropy uses wavelet transforms to implicitly include an image's two-dimensional structural information into the algorithm's entropy calculation.An evaluation using synthetic data shows that the deconvolution algorithm reduces the maximum individual pixel error from 90.01 to 0.34%. Deconvolution of MODIS data is shown to resolve significant features and is most effective in regions where there are large changes in radiance such as coastal zones or contrasting land covers.

Protecting deep‐sea coral‐based vulnerable marine ecosystems (VMEs) from human impacts, particularly bottom trawling, is a major conservation challenge in world oceans. Management processes for these ecosystems are weakened by key uncertainties that could be substantially addressed by having much greater volumes of quantitative image‐derived data that detail the distribution and abundance of coral reefs and the nature of impacts upon them. Considerably greater volumes of data could be available if the resource costs of image annotation are reduced. In this paper we propose a solution: a deep learning system capable of automatically identifying reef‐building stony corals amongst other seabed substrata in much larger volumes of seabed imagery than was previously possible. Using a previously annotated dataset, we trained a convolutional neural network on approximately 70,000 classified images (‘snips’) comprising six benthic substrate classes, including reef‐building stony coral—‘coral matrix’. Model performance improvements, chiefly by dataset cleaning, transfer learning and hyperparameter optimisation, resulted in the final trained model achieving validation accuracy of 98.19%. The classification was robust: benthic substrate types were accurately differentiated, and in some cases more consistently than was achieved by human annotators. Synthesis and applications. The availability of much larger volumes of automatically annotated image‐derived data will improve spatial management of impacts on coral‐based VMEs in the deep sea by (1) improved cross‐validation and performance of spatial models required to predict coral distribution and abundance over the large scales of managed areas, and (2) establishing empirical relationships between coral abundance on the seabed and coral bycatch landed during fishing operations.

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