Automated Activity Estimation of the Cold-Water Coral Lophelia pertusa by Multispectral Imaging and Computational Pixel Classification

The cold-water coral Lophelia pertusa builds up bioherms that sustain high biodiversity in the deep ocean worldwide. Photographic monitoring of the polyp activity represents a helpful tool to characterize the health status of the corals and to assess anthropogenic impacts on the microhabitat. Discriminating active polyps from skeletons of white Lophelia pertusa is usually time-consuming and error-prone due to their similarity in color in common RGB camera footage. Acquisition of finer resolved spectral information might increase the contrast between the segments of polyps and skeletons, and therefore could support automated classification and accurate activity estimation of polyps. For recording the needed footage, underwater multispectral imaging systems can be used, but they are often expensive and bulky. Here we present results of a new, light-weight, compact and low-cost deep-sea tunable LED-based underwater multispectral imaging system (TuLUMIS) with eight spectral channels. A brunch of healthy white Lophelia pertusa was observed under controlled conditions in a laboratory tank. Spectral reflectance signatures were extracted from pixels of polyps and skeletons of the observed coral. Results showed that the polyps can be better distinguished from the skeleton by analysis of the eight-dimensional spectral reflectance signatures compared to three-channel RGB data. During a 72-hour monitoring of the coral with a half-hour temporal resolution in the lab, the polyp activity was estimated based on the results of the multispectral pixel classification using a support vector machine (SVM) approach. The computational estimated polyp activity was consistent with that of the manual annotation, which yielded a correlation coefficient of 0.957

Underwater Hyperspectral Imaging (UHI): a review of systems and applications for proximal seafloor ecosystem studies

Marine ecosystem monitoring requires observations of its attributes at different spatial and temporal scales that traditional sampling methods (e.g., RGB imaging, sediment cores) struggle to efficiently provide. Proximal optical sensing methods can fill this observational gap by providing observations of, and tracking changes in, the functional features of marine ecosystems non-invasively. Underwater hyperspectral imaging (UHI) employed in proximity to the seafloor has shown a further potential to monitor pigmentation in benthic and sympagic phototrophic organisms at small spatial scales (mm–cm) and for the identification of minerals and taxa through their finely resolved spectral signatures. Despite the increasing number of studies applying UHI, a review of its applications, capabilities, and challenges for seafloor ecosystem research is overdue. In this review, we first detail how the limited band availability inherent to standard underwater cameras has led to a data analysis “bottleneck” in seafloor ecosystem research, in part due to the widespread implementation of underwater imaging platforms (e.g., remotely operated vehicles, time-lapse stations, towed cameras) that can acquire large image datasets. We discuss how hyperspectral technology brings unique opportunities to address the known limitations of RGB cameras for surveying marine environments. The review concludes by comparing how different studies harness the capacities of hyperspectral imaging, the types of methods required to validate observations, and the current challenges for accurate and replicable UHI research

Deep-Sea Robotic Survey and Data Processing Methods for Regional-Scale Estimation of Manganese Crust Distribution

Manganese crusts (Mn-crusts) are a type of mineral deposit that exists on the surface of seamounts and guyots at depths of >800 m. We have developed a method to efficiently map their distribution using data collected by autonomous underwater vehicles and remotely operated vehicles. Volumetric measurements of Mn-crusts are made using a high-frequency subsurface sonar and a 3-D visual mapping instrument mounted on these vehicles. We developed an algorithm to estimate Mn-crust distribution by combining continuous subsurface thickness measurements with the exposed surface area identified in 3-D maps. This is applied to data collected from three expeditions at Takuyo Daigo seamount at depths of ~1400 m. The transects add to ~11 km in length with 12 510 m 2 mapped. The results show that 52% of the surveyed area is covered by Mn-crusts with a mean thickness of 69.6 mm. The mean Mn-crust occurrence is 69.6 kg/m 2 with a maximum of 204 kg/m 2 in the mapped region. The results are consistent with estimates made from samples retrieved from the area, showing more detailed distribution patterns and having significantly lower uncertainty bounds for regional-scale Mn-crust inventory estimation

Living up to the hype of hyperspectral aquatic remote sensing: science, resources and outlook

Intensifying pressure on global aquatic resources and services due to population growth and climate change is inspiring new surveying technologies to provide science-based information in support of management and policy strategies. One area of rapid development is hyperspectral remote sensing: imaging across the full spectrum of visible and infrared light. Hyperspectral imagery contains more environmentally meaningful information than panchromatic or multispectral imagery and is poised to provide new applications relevant to society, including assessments of aquatic biodiversity, habitats, water quality, and natural and anthropogenic hazards. To aid in these advances, we provide resources relevant to hyperspectral remote sensing in terms of providing the latest reviews, databases, and software available for practitioners in the field. We highlight recent advances in sensor design, modes of deployment, and image analysis techniques that are becoming more widely available to environmental researchers and resource managers alike. Systems recently deployed on space- and airborne platforms are presented, as well as future missions and advances in unoccupied aerial systems (UAS) and autonomous in-water survey methods. These systems will greatly enhance the ability to collect interdisciplinary observations on-demand and in previously inaccessible environments. Looking forward, advances in sensor miniaturization are discussed alongside the incorporation of citizen science, moving toward open and FAIR (findable, accessible, interoperable, and reusable) data. Advances in machine learning and cloud computing allow for exploitation of the full electromagnetic spectrum, and better bridging across the larger scientific community that also includes biogeochemical modelers and climate scientists. These advances will place sophisticated remote sensing capabilities into the hands of individual users and provide on-demand imagery tailored to research and management requirements, as well as provide critical input to marine and climate forecasting systems. The next decade of hyperspectral aquatic remote sensing is on the cusp of revolutionizing the way we assess and monitor aquatic environments and detect changes relevant to global communities

OBJECT PERCEPTION IN UNDERWATER ENVIRONMENTS: A SURVEY ON SENSORS AND SENSING METHODOLOGIES

Underwater robots play a critical role in the marine industry. Object perception is the foundation for the automatic operations of submerged vehicles in dynamic aquatic environments. However, underwater perception encounters multiple environmental challenges, including rapid light attenuation, light refraction, or backscattering effect. These problems reduce the sensing devices’ signal-to-noise ratio (SNR), making underwater perception a complicated research topic. This paper describes the state-of-the-art sensing technologies and object perception techniques for underwater robots in different environmental conditions. Due to the current sensing modalities’ various constraints and characteristics, we divide the perception ranges into close-range, medium-range, and long-range. We survey and describe recent advances for each perception range and suggest some potential future research directions worthy of investigating in this field

Opportunities for seagrass research derived from remote sensing : a review of current methods

Seagrass communities provide critical ecosystem and provisioning services for both human populations and a wide range of associated species globally. However, it has been reported that seagrass area is decreasing at a rapid rate in many parts of the world, mostly due to anthropogenic activities including global change (pollution and climate change). The aim of this review article is to highlight the range of current tools for studying seagrasses as well as identify the benefits and limitations of a range of remote sensing and traditional methodologies. This paper provides a discussion of the ecological importance of seagrass meadows, and recent trends and developments in seagrass research methods are discussed including the use of satellite images and aerial photographs for seagrass monitoring and various image processing steps that are frequently utilised for seagrass mapping. The extensive use of various optical, Radar and LiDAR data for seagrass research in recent years has also been described in detail. The review concludes that the recent explosion of new methods and tools available from a wide range of platforms combined with the recent recognition of the importance of seagrasses provides the research community with an excellent opportunity to undertake a range of timely research. This research should include mapping the extent and distribution of seagrasses, identifying the drivers of change and factors that confer resilience, as well as quantification of the ecosystem services provided. Whilst remotely sensed data provides an important new tool it should be used in conjunction with traditional methods for validation and with a knowledge of the limitations of results and careful interpretation

Object-based mapping of temperate marine habitats from multi-resolution remote sensing data

PhD ThesisHabitat maps are needed to inform marine spatial planning but current methods of field survey and data interpretation are time-consuming and subjective. Object-based image analysis (OBIA) and remote sensing could deliver objective, cost-effective solutions informed by ecological knowledge. OBIA enables development of automated workflows to segment imagery, creating ecologically meaningful objects which are then classified based on spectral or geometric properties, relationships to other objects and contextual data. Successfully applied to terrestrial and tropical marine habitats for over a decade, turbidity and lack of suitable remotely sensed data had limited OBIA’s use in temperate seas to date. This thesis evaluates the potential of OBIA and remote sensing to inform designation, management and monitoring of temperate Marine Protected Areas (MPAs) through four studies conducted in English North Sea MPAs. An initial study developed OBIA workflows to produce circalittoral habitat maps from acoustic data using sequential threshold-based and nearest neighbour classifications. These methods produced accurate substratum maps over large areas but could not reliably predict distribution of species communities from purely physical data under largely homogeneous environmental conditions. OBIA methods were then tested in an intertidal MPA with fine-scale habitat heterogeneity using high resolution imagery collected by unmanned aerial vehicle. Topographic models were created from the imagery using photogrammetry. Validation of these models through comparison with ground truth measurements showed high vertical accuracy and the ability to detect decimetre-scale features. The topographic and spectral layers were interpreted simultaneously using OBIA, producing habitat maps at two thematic scales. Classifier comparison showed that Random Forests Abstract ii outperformed the nearest neighbour approach, while a knowledge-based rule set produced accurate results but requires further research to improve reproducibility. The final study applied OBIA methods to aerial and LiDAR time-series, demonstrating that despite considerable variability in the data, pre- and post-classification change detection methods had sufficient accuracy to monitor deviation from a background level of natural environmental fluctuation. This thesis demonstrates the potential of OBIA and remote sensing for large-scale rapid assessment, detailed surveillance and change detection, providing insight to inform choice of classifier, sampling protocol and thematic scale which should aid wider adoption of these methods in temperate MPAs.Natural Environment Research Council and Natural Englan

Critical Habitats and Biodiversity: Inventory, Thresholds and Governance

The High Level Panel for Sustainable Ocean Economy (https://oceanpanel.org/) has commissioned a series of “Blue Papers” to explore pressing challenges at the nexus of the ocean and the economy. This paper is part of a series of 16 papers to be published between November 2019 and October 2020. It addresses how multiple human impacts will impact biodiversity underpinning ecosystem services such as marine fisheries, aquaculture, coastal protection and tourism. The paper examines the distribution of marine species and critical marine habitats around the world; analyses trends in drivers, pressures, impacts and response; and establishes thresholds for protecting biodiversity hot spots, and indicators to monitor change. From this scientific base, it assesses the current legal framework and available tools for biodiversity protection, current gaps in ocean governance and management and the implications for achieving a sustainable ocean economy tailored to individual coastal states grouped by social indicators

Coregistered hyperspectral and stereo image seafloor mapping from an autonomous underwater vehicle

We present a new method for in situ high-resolution hyperspectral mapping of the seafloor utilizing a spectrometer colocated and coregistered with a high-resolution color stereo camera system onboard an autonomous underwater vehicle (AUV). Hyperspectral imagery data have been used extensively for mapping and distinguishing marine seafloor habitats and organisms from above-water platforms (such as satellites and aircraft), but at low spatial resolutions and at shallow water depths (<10 m). The use of hyperspectral sensing from in-water platforms (such as AUVs) has the potential to provide valuable habitat data in deeper waters and with high spatial resolution. Challenges faced by in-water hyperspectral imaging include difficulties in correcting for water column effects and the spatial registration of point/line-scan hyperspectral sensor measurements. The methods developed in this paper overcome these issues through coregistration with a high spatial resolution, stereo color camera, and precise modeling and compensation of the water column properties that attenuate hyperspectral signals. We integrated two spectrometers into our SeaBED class AUV, and one on-board a support surface vessel to measure and estimate the effects of light passing through the water column. Spatial calibration of the spectrometers/stereo cameras and the synchronized acquisition of both sensors allowed for spatial registration of the resulting hyperspectral reflectance profiles. We demonstrate resulting hyperspectral imagery maps with a spatial resolution of 30 cm over large areas of the seafloor that are not adversely effected by above-water conditions (such as cloud cover) that would typically prevent the use of remote-sensing methods. Results are presented from an AUV mapping survey of a coral reef ecosystem over Pakhoi Bank on the Great Barrier Reef, Queensland, Australia, demonstrating the ability to reconstruct hyperspectral reflectance profiles for a diverse range of abiotic and biotic coverage types including sand, corals, seagrass, and algae. Profiles are then used to automatically classify different coverage types with a 10-fold cross validation accuracy of 91.99% using a linear support vector machine (SVM)