Limitations of predicting substrate classes on a sedimentary complex but morphologically simple seabed

The ocean floor, its species and habitats are under pressure from various human activities. Marine spatial planning and nature conservation aim to address these threats but require sufficiently detailed and accurate maps of the distribution of seabed substrates and habitats. Benthic habitat mapping has markedly evolved as a discipline over the last decade, but important challenges remain. To test the adequacy of current data products and classification approaches, we carried out a comparative study based on a common dataset of multibeam echosounder bathymetry and backscatter data, supplemented with groundtruth observations. The task was to predict the spatial distribution of five substrate classes (coarse sediments, mixed sediments, mud, sand, and rock) in a highly heterogeneous area of the south-western continental shelf of the United Kingdom. Five different supervised classification methods were employed, and their accuracy estimated with a set of samples that were withheld. We found that all methods achieved overall accuracies of around 50%. Errors of commission and omission were acceptable for rocky substrates, but high for all sediment types. We predominantly attribute the low map accuracy regardless of mapping approach to inadequacies of the selected classification system, which is required to fit gradually changing substrate types into a rigid scheme, low discriminatory power of the available predictors, and high spatial complexity of the site relative to the positioning accuracy of the groundtruth equipment. Some of these issues might be alleviated by creating an ensemble map that aggregates the individual outputs into one map showing the modal substrate class and its associated confidence or by adopting a quantitative approach that models the spatial distribution of sediment fractions. We conclude that further incremental improvements to the collection, processing and analysis of remote sensing and sample data are required to improve map accuracy. To assess the progress in benthic habitat mapping we propose the creation of benchmark datasets

Benthic habitat map of the southern Adriatic Sea (Mediterranean Sea) from object-based image analysis of multi-source acoustic backscatter data

© The Author(s), 2021. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Prampolini, M., Angeletti, L., Castellan, G., Grande, V., Le Bas, T., Taviani, M., & Foglini, F. Benthic habitat map of the southern Adriatic Sea (Mediterranean Sea) from object-based image analysis of multi-source acoustic backscatter data. Remote Sensing, 13(15), (2021): 2913, https://doi.org/10.3390/rs13152913.A huge amount of seabed acoustic reflectivity data has been acquired from the east to the west side of the southern Adriatic Sea (Mediterranean Sea) in the last 18 years by CNR-ISMAR. These data have been used for geological, biological and habitat mapping purposes, but a single and consistent interpretation of them has never been carried out. Here, we aimed at coherently interpreting acoustic data images of the seafloor to produce a benthic habitat map of the southern Adriatic Sea showing the spatial distribution of substrates and biological communities within the basin. The methodology here applied consists of a semi-automated classification of acoustic reflectivity, bathymetry and bathymetric derivatives images through object-based image analysis (OBIA) performed by using the ArcGIS tool RSOBIA (Remote Sensing OBIA). This unsupervised image segmentation was carried out on each cruise dataset separately, then classified and validated through comparison with bottom samples, images, and prior knowledge of the study areas.This research was funded by EUROSTRATAFORM (EC contract no. EVK3-CT-2002-00079), EU-FP-VI HERMES (GOCE-CT-2005-511234-1), EU-FP-VII HERMIONE (contract no. 226354) and COCONET (Grant agreement no: 287844); Convenzione MATTM-CNR per i Programmi di Monitoraggio per la Direttiva sulla Strategia Marina (MSFD, Art. 11, Dir. 2008/56/CE); Italian Flag Project Ritmare (Ricerca Italiana per il Mare); MAGIC (Accordo di Programma Quadro Consiglio Nazionale delle Ricerche—CNR, Dipartimento della protezione civile della Presidenza del Consiglio dei Ministri); MIUR-PRIN 2009 “Carbonate conduits linked to hydrocarbons enriched seepages” and MIUR-PRIN 2017 GLIDE 2017FREXZY. This paper contributes to H2020 Projects EVER-EST (Grant agreement no: 674907) and RELIANCE (Grant agreement no: 101017501). This is ISMAR-CNR contribution number 1975

Control of the repeatability of high frequency multibeam echosounder backscatter by using natural reference areas

The increased use of backscatter measurements in time series for environmental monitoring necessitates the comparability of individual results. With the current lack of pre-calibrated multibeam echosounder systems for absolute backscatter measurement, a pragmatic solution is the use of natural reference areas for ensuring regular assessment of the backscatter measurement repeatability. This method mainly relies on the assumption of a sufficiently stable reference area regarding its backscatter signature. The aptitude of a natural area to provide a stable and uniform backscatter response must be carefully considered and demonstrated by a sufficiently long time-series of measurements. Furthermore, this approach requires a strict control of the acquisition and processing parameters. If all these conditions are met, stability check and relative calibration of a system are possible by comparison with the averaged backscatter values for the area. Based on a common multibeam echosounder and sampling campaign completed by available bathymetric and backscatter time series, the suitability as a backscatter reference area of three different candidates was evaluated. Two among them, Carré Renard and Kwinte, prove to be excellent choices, while the third one, Western Solent, lacks sufficient data over time, but remains a valuable candidate. The case studies and the available backscatter data on these areas prove the applicability of this method. The expansion of the number of commonly used reference areas and the growth of the number of multibeam echosounder controlled thereon could greatly contribute to the further development of quantitative applications based on multibeam echosounder backscatter measurements

Interpreting monitoring data for shoreline and geohazard mapping

The demand for marine-related spatial information has become increasingly apparent in recent years at a European and national scale, due to the increased pressures on the sea-floor environments and marine resources of UK territorial waters. The advent of economically viable swath bathymetry data acquisition in the coastal zone and effective collaborative partnerships between the Channel Coastal Observatory, Maritime and Coastguard Agency, UK Hydrographic Office, British Geological Survey and academic institutions, have opened up new opportunities to produce a robust scientific evidence base to inform integrated coastal zone management objectives and contribute to wider scientific initiatives. Interpretation of high-quality bathymetric data, acoustic backscatter and ground-truthing data allows zones of exposed bedrock, rock outcrops and pinnacles to be identified, along with areas of mobility or stability of surficial sediments. Temporal and spatial analyses of coastal and marine monitoring datasets also contribute to improved understanding of interactions between natural coastal process and coastal-defence and beach-management operations. Furthermore, developments in three-dimensional mapping techniques and visualisation technologies have enabled seamless high-resolution coastal geology maps to be re-interpreted and extended offshore, providing a more complete picture of the baseline geology, physical properties, structure and geohazards in the coastal and nearshore zone. The full paper details the methodology developed to produce a range of indicative marine mapping layers, and presents examples from eastern and southern England where marine-related spatial data has contributed to the multi-disciplinary scientific evidence base to inform development of UK marine policy and planning, coastal management and coastal zone geological mappin

Application of random-forest machine learning algorithm for mineral predictive mapping of Fe-Mn crusts in the World Ocean

Mineral prospectivity mapping constitutes an efficient tool for delineating areas of highest interest to guide future exploration. Multiple knowledge-driven approaches have been applied for the creation of prospectivity maps for deep-sea ferromanganese (Fe-Mn) crusts over the last decades. The results of a data-driven approach making use of an extensive data collection exercise on occurrences of Fe-Mn crusts in the World Ocean and recent increase in global marine datasets are presented. A Random Forest machine learning algorithm is applied, and results compared with previously established expert-driven maps. Optimal predictive conditions for the algorithm are observed for (i) a forest size superior to a hundred trees, (ii) a training dataset larger than 10%, and (iii) a number of predictors to be used as nodes superior to two. The confusion matrix and out-of-bag errors on the remaining unused data highlight excellent predictive capabilities of the trained model with a prediction accuracy for Fe-Mn crusts of 87.2% and 98.2% for non-crusts locations, with a Kohen’s K index of 0.84, validating its application for prediction at the World scale. The slope of the seafloor, sediment thickness, sediment type, biological productivity, and abyssal mountain constitute the five strongest explanatory variables in predicting the occurrence of Fe-Mn crusts. Most ‘hand-drawn’ knowledge-driven prospective areas are also considered prospective by the random forest algorithm with notable exceptions along the coast of the American continent. However, poor correlation is observed with knowledge-driven GIS-based criterion mapping as the Random Forest considers un-prospective most target areas from the GIS approach. Overall, the Random Forest prediction performs better in predicting a high chance of Fe-Mn crust occurrence in ISA licensed area than the GIS approach, which constitutes an external validation of the predictive quality of the random forest model

Mesophotic depth biogenic accumulations (“biogenic mounds”) offshore the Maltese Islands, Central Mediterranean Sea

The mesophotic domain is a poorly explored part of the oceans, notably in the Mediterranean Sea. Benthic communities in these depths are not well documented and as such are under higher risk from anthropogenic impacts. Hard substrate habitats in this depth window are not common and are a key ecotope. The Malta Plateau in the central Mediterranean, which is characterized by low sedimentation rates, offers a potentially unexplored domain for these features. Bathymetric and backscatter data offshore of the eastern coast of the island of Malta were used to identify > 1,700 small structures in mesophotic depths. These structures were verified to be biogenic mounds by dives. The mounds extend from several meters to tens of meters in diameter and occur in two main depth windows −40 to 83 meters below present sea level (mbpsl) and 83–120 mbpsl—each formed probably in a different stage during the last glacial cycle. The mounds are composed of interlocking bioconstruction by encrusting organisms and are colonized by sponges and various cold water corals (most of which are protected; e.g., Madrepora oculata). This unique and important habitat is currently under grave threat by human activity, most immediately by trawling and anchoring activity

Seabed mapping in the Pelagie Islands marine protected area (Sicily Channel, southern Mediterranean) using Remote Sensing Object Based Image Analysis (RSOBIA)

In this paper we present the seabed maps of the shallow-water areas of Lampedusa and Linosa, belonging to the Pelagie Islands Marine Protected Area. Two surveys were carried out (“Lampedusa 2015” and “Linosa 2016”) to collect bathymetric and acoustic backscatter data through the use of a Reson SeaBat 7125 high-resolution multibeam system. Ground-truth data, in the form of grab samples and diver video-observations, were also collected during both surveys. Sediment samples were analyzed for grain size, while video images were analyzed and described revealing the acoustic seabed and other bio-physical characteristics. A map of seabed classification, including sediment types and seagrass distribution, was produced using the tool Remote Sensing Object Based Image Analysis (RSOBIA) by integrating information derived from backscatter data and bathy-morphological features, validated by ground-truth data. This allows to create a first seabed maps (i.e. benthoscape classification), of Lampedusa and Linosa, at scale 1:20 000 and 1:32 000, respectively, that will be checked and implemented through further surveys. The results point out a very rich and largely variable marine ecosystem on the seabed surrounding the two islands, with the occurrence of priority habitats, and will be of support for a more comprehensive maritime spatial planning of the Marine Protected Area

Characteristics of shallow and mesophotic environments of the Pemba Channel, Tanzania : implications for management and conservation

Information on the spatial distribution of habitats and vulnerable species is important for conservation planning. In particular, detailed knowledge on connectivity of marine ecosystems in relation to depth and seafloor characteristics is crucial for any proposed conservation and management actions. Yet, the bulk of the seafloor remains undersampled, unstudied and unmapped, thereby limiting our understanding of connections between shallow and deep-water communities. Recent studies on mesophotic coral ecosystems (MCEs) have highlighted the western Indian Ocean as a particularly understudied marine region. Here we utilise an autonomous underwater vehicle (AUV) to collect in-situ temperature, oxygen concentration, bathymetry, acoustic backscatter and photographic data on benthic communities from shallow (<30 m) and mesophotic (30-150 m) depths at selected sites in the Greater Pemba Channel, Tanzania . Further, we use generalised additive models (GAMs) to determine useful predictors of substratum (hard and sand) and benthic community type (coral, turf algae, fleshy algae, fish). Our results revealed the presence of a complex seafloor characterised by pockmarks, steep slopes, submarine walls, and large boulders. Photographs confirmed the presence of MCE composed of corals, algae and fishes on the eastern margins of the Pemba Channel. The GAMs on the presence and absence of benthic community explained 35% to 91% of the deviance in fish and fleshy algae assemblages, respectively. Key predictors of the distribution of hard substrata and the coral reef communities were depth, showing the upper boundary of MCEs present at 30-40 m, and seafloor slope that showed more occurrences on steep slopes. The upper 100 m of water column had stable temperatures (25-26°C) and oxygen concentrations (220- 235 μmol/l). We noted the presence of submarine walls, steeply inclined bedrock, which appeared to support a highly bio-diverse community that may be worthy of particular conservation measures. Our results also highlight the capability of using marine robotics, particularly autonomous vehicles, to fill the knowledge gap for areas not readily accessible with surface vessels, and their potential application in the initial survey and subsequent monitoring of Marine Protected Areas

Monitoring mosaic biotopes in a marine conservation zone by autonomous underwater vehicle

The number of marine protected areas (MPAs) has increased dramatically in the last decade and poses a major logistic challenge for conservation practitioners in terms of spatial extent and the multiplicity of habitats and biotopes that now require assessment. Photographic assessment by autonomous underwater vehicle (AUV) enables the consistent description of multiple habitats, in our case including mosaics of rock and sediment. As a case study, we used this method to survey the Greater Haig Fras marine conservation zone (Celtic Sea, northeast Atlantic). We distinguished 7 biotopes, detected statistically significant variations in standing stocks, species density, species diversity, and faunal composition, and identified significant indicator species for each habitat. Our results demonstrate that AUV‐based photography can produce robust data for ecological research and practical marine conservation. Standardizing to a minimum number of individuals per sampling unit, rather than to a fixed seafloor area, may be a valuable means of defining an ecologically appropriate sampling unit. Although composite sampling represents a change in standard practice, other users should consider the potential benefits of this approach in conservation studies. It is broadly applicable in the marine environment and has been successfully implemented in deep‐sea conservation and environmental impact studies. Without a cost‐effective method, applicable across habitats, it will be difficult to further a coherent classification of biotopes or to routinely assess their conservation status in the rapidly expanding global extent of MPAs