Benthic Habitat Mapping Research Articles

First High‐Resolution Benthic Habitat Map From the Greenland Shelf (Disko Bay Pilot Study)

AbstractA healthy ocean where marine habitats and ecosystems are mapped and protected is one of the UN's Sustainable Development Goals to sustainably use marine resources. Our study presents the first high‐resolution benthic habitat map from Greenland integrating analyses of multibeam bathymetry and backscatter data, and ground‐truth data including video sled, drop camera and day grab. The pilot area of 30 × 20 km is located on the continental shelf in central Disko Bay, West Greenland and all data were collected in a single, 10‐day survey. Multibeam bathymetry data were gridded to a 10 × 10 m resolution, whereas backscatter mosaic was built from a 1 × 1 m grid cell to obtain higher resolution manifestation of seafloor properties. Ground‐truth data consisted of 14 video transects, 17 drop camera deployments, and 17 sediment samples. Our results were verified with the published shallow seismic and vibrocore data from the Disko Bay region to link the geological background with the sedimentary environment. We distinguished five physical habitats in the area, based on the distribution of sediment types, water depth with general water masses and morphology. In addition, numerous gas seeps alongside pockmarks were observed in the area, as well as recent iceberg ploughmarks. The identified habitats were associated with two basic communities of benthic fauna, linked primarily to the distribution of sediments and representing hard bottom habitats (sessile fauna) and soft bottom habitats (shrimp/polychaetes). Our study is the first step toward mapping the entire seafloor of Disko Bay to provide a scientific context for the management of seafloor and marine resources.

Regional High-Resolution Benthic Habitat Data from Planet Dove Imagery for Conservation Decision-Making and Marine Planning

High-resolution benthic habitat data fill an important knowledge gap for many areas of the world and are essential for strategic marine conservation planning and implementing effective resource management. Many countries lack the resources and capacity to create these products, which has hindered the development of accurate ecological baselines for assessing protection needs for coastal and marine habitats and monitoring change to guide adaptive management actions. The PlanetScope (PS) Dove Classic SmallSat constellation delivers high-resolution imagery (4 m) and near-daily global coverage that facilitates the compilation of a cloud-free and optimal water column image composite of the Caribbean’s nearshore environment. These data were used to develop a first-of-its-kind regional thirteen-class benthic habitat map to 30 m water depth using an object-based image analysis (OBIA) approach. A total of 203,676 km2 of shallow benthic habitat across the Insular Caribbean was mapped, representing 5% coral reef, 43% seagrass, 15% hardbottom, and 37% other habitats. Results from a combined major class accuracy assessment yielded an overall accuracy of 80% with a standard error of less than 1% yielding a confidence interval of 78–82%. Of the total area mapped, 15% of these habitats (31,311.7 km2) are within a marine protected or managed area. This information provides a baseline of ecological data for developing and executing more strategic conservation actions, including implementing more effective marine spatial plans, prioritizing and improving marine protected area design, monitoring condition and change for post-storm damage assessments, and providing more accurate habitat data for ecosystem service models.

Representative benthic habitat mapping on Lovina coral reefs in Northern Bali, Indonesia

Abstract. Kartikasari A, Pristianto T, Hanintyo R, Ampou EE, Wibawa TA, Borneo BB. 2021. Representative benthic habitat mapping on Lovina coral reefs in Northern Bali, Indonesia. Biodiversitas 22: 4766-4774. Satellite optical imagery datasets integrated with in situ measurements are widely used to derive the spatial distribution of various benthic habitats in coral reef ecosystems. In this study, an approach to estimate spatial coverage of those habitats based on observation derived from Sentinel-2 optical imagery and a field survey, is presented. This study focused on the Lovina coral reef ecosystem of Northern Bali, Indonesia to support deployment of artificial reefs within the Indonesian Coral Reef Garden (ICRG) programme. Three specific locations were explored: Temukus, Tukad Mungga, and Baktiseraga waters. Spatial benthic habitat coverages of these three waters was estimated based on supervised classification techniques using 10m bands of Sentinel-2 imagery and the medium scale approach (MSA) transect method of in situ measurement.The study indicates that total coverage of benthic habitat is 61.34 ha, 25.17 ha, and 27.88 ha for Temukus, Tukad Mungga, and Baktiseraga waters, respectively. The dominant benthic habitat of those three waters consists of sand, seagrass, coral, rubble, reef slope and intertidal zone. The coral reef coverage is 29.48 ha (48%) for Temukus covered by genus Acropora, Isopora, Porites, Montipora, Pocillopora. The coverage for Tukad Mungga is 8.69 ha (35%) covered by genus Acropora, Montipora, Favia, Psammocora, Porites, and the coverage for Baktiseraga is 11.37 ha (41%) covered by genus Montipora sp, Goniastrea, Pavona, Platygyra, Pocillopora, Porites, Acropora, Leptoseris, Acropora, Pocillopora, Fungia. The results are expected to be suitable as supporting data in restoring coral reef ecosystems in the northern part of Bali, especially in Buleleng District.

Quantifying acoustic backscatter of seafloor using multibeam echosounder

Multibeam echosounder is one of the most important technology for carrying out hydrographic surveys, benthic habitat mapping, marine fisheries, and seafloor surveys. The advantage of multibeam echosounder were produced simultaneously bathymetric and acoustic backscatter. The purpose of this research was to process and to analyze multibeam echosounder data for mapping the seafloor and quantifying acoustic backscatter. Bathymetric data was processed used the Combined Uncertainty and Bathymetry Estimator (CUBE) method. The analysis of seabed type used Angular Response Analysis (ARA) and Sediment Analysis Tool (SAT). Seawater depth in the survey area was measured from 11.2 m to 45.3 m. Analysis of seafloor classification consists of clay, silt, sand, and rock bottom. The highest acoustic backscatter was rock bottom and the lowest was clay bottom.

Identifying Habitats of Conservation Priority in the São Vicente Submarine Canyon in Southwestern Portugal

Identifying vulnerable marine habitats is fundamental to promote evidence-based management and a sustainable use of our oceans. Although progress in the mapping of marine benthic habitats has been made, complex ecosystems such as submarine canyons remain largely unexplored, hampering evidence-based management from taking place in these ubiquitous features worldwide. Data from a remotely operated vehicle (ROV) survey in the upper São Vicente Submarine Canyon in southwestern Portugal from 2011, was analysed to carry out a comprehensive assessment of megabenthic habitats, particularly those of conservation concern occuring between 90 and 560 m. Underwater videos from three exploratory dives, covering a total of 8.8 kilometers, were analyzed through multivariate and visual assessments, and the identified habitats were assessed against a set of criteria derived from current definitions of priority habitats. Thirteen potential priority habitats were characterized, out of which four distinct “coral gardens” and one “deep-sea sponge aggregation” are aligned with OSPAR definitions of priority habitats, and one corresponds to the habitat of a strictly protected sea urchin listed in the Habitats Directive. Three circalittoral sponge aggregations were also suggested based on the abundance of indicator species. The specific composition and diversity of priority habitats as described here, has not yet been described in any other canyon in the NE Atlantic region. Depth was revealed to be particularly important for the distribution of habitats, followed by relief and substratum. The approach presented in this study contributes to improve the understanding about submarine canyons, which is paramount to implement knowledge-based protection measures. Furthermore, it can be used to support future mapping efforts of deep-sea environments such as submarine canyons.

Multispectral low altitude remote sensing of wild oyster reefs

Although wild oyster reefs are invasive and are trophic competitors, they are an essential component of intertidal rocky reef systems that support a wide range of biodiversity in some intertidal estuaries. Despite their importance, their population locally and globally are declining, reflecting variations such as increased predation and anthropogenic influence in an estuary, such as excessive nutrients discharge. In addition, spatial assessment and mapping wild oyster reefs can be challenging due to complex and rugged structures that are challenging to access using conventional in-situ, time-extensive techniques. Therefore, the application of remote sensing for ecology and conservation of benthic habitats has emerged as a powerful technique to expedite many challenges. This study investigated VIS (visible spectrum) and VIS+NIR (visible + near-infrared spectrum) remote sensing for spatial assessment and mapping wild oyster reefs in a rocky intertidal reef at low tide. In addition, the study assessed the spectral reflectance signatures of wild oyster reefs from RPAS aerial imagery to in-situ spectral signatures. Although the VIS orthomosaic had a higher spatial resolution, wild oyster reefs detection was less efficient. In contrast, oyster reefs were correctly classified in the VIS+NIR imagery achieving an overall accuracy of 85%. This accuracy implies that the spectral resolution has been more critical to detect and correctly classify oyster reefs than spatial resolution. This remote sensing technique for ecology and conservation offers scale-appropriate spatial assessment, monitoring, and mapping of benthic habitats in challenging and inaccessible marine environments. Furthermore, this technique can be complemented with in-situ and other remote sensing datasets to enhance our understanding of the spatial dynamics of near-shore marine environments.

Cloud-Native Coastal Turbid Zone Detection Using Multi-Temporal Sentinel-2 Data on Google Earth Engine

The lack of clarity in turbid coastal waters interferes with light attenuation and hinders remotely sensed studies in aquatic ecology such as benthic habitat mapping and bathymetry estimation. Although turbid water column corrections can be applied on regions with seasonal turbidity by performing multi-temporal analysis, different approaches are needed in regions where the water is constantly turbid or only exhibits subtle turbidity variations through time. This study aims to detect these turbid zones (TZs) in optically shallow coastal waters using multi-temporal Sentinel-2 surface reflectance datasets to improve the aforementioned studies. The herein framework can be paired with other aquatic ecology remote sensing studies to establish the clear water focus area and can also be used by decision makers to identify rehabilitation areas. We selected the coastlines of Guinea-Bissau, Tunisia, and west Madagascar as our case studies which feature wide-ranging turbidity intensities across tropical, subtropical, and Mediterranean waters and applied three different methods for the TZ detection: Otsu’s method for bimodal thresholding, linear spectral unmixing, and Random Forest (RF) machine learning method on Google Earth Engine as an end-to-end process. Based on our experiments, the RF method yields good results in all study regions with overall accuracies ranging between 88 and 96% and F1-scores between 0.87 and 0.96. TZ detection is highly site-specific due to the inter-class variability that is mainly affected by the nature of the suspended materials and the environmental characteristics of the site.

Distribution of macroalgae in the area of Calvi (Corsica).

BackgroundMacroalgae play a structuring role in benthic ecosystems, which makes it very important to monitor their cover rates and study their community structures and changes in time. Such studies are usually led by autonomous divers and often do not generate sufficient data to provide enough material for strategically-sound conservation plans. This paper describes the dataset generated in the framework of the evaluation of the potential of a complementary data acquisition method: annotating videos transects obtained using an underwater Remotely Operated Vehicle (ROV).The focus was on Cystoseirabrachycarpa, together with the abundance of several other macroalgae species, which could be identified using the ROV images after validation through specimen identification. Furthermore, in order to allow future uses, such as monitoring the status of colonisation of the invasive algae Caulerpacylindracea, the ROV was sent to some deeper transects on sedimentary habitats (40 m) below the Posidonia meadows lower limits.New informationThe project, while providing some interesting insights on using a ROV as a tool to study marine biodiversity, generated a dataset for the distribution of 19 macroalgae on both standardised and new transects in the Bay of Calvi (Katz et al. 2021). The observed species of macroalgae included: Acetabulariaacetabulum, Amphiroarigida, Caulerpaprolifera, Caulerpacylindracea, Codiumbursa, Colpomeniasinuosa, Corallinales (order), Cystoseirabrachycarpa, Cystoseiracrinita, Cystoseiraspinosa, Cystoseirazosteroides, Dictyota (genus), Flabelliapetiolata, Halopteris (genus), Halopterisscoparia, Jania (genus), Osmundariavolubilis, Padina pavonica and Peyssonneliasquamaria.The videos also showed that the invasive algae Caulerpacylindracea has spread between 2016 and 2019 and that more focused studies should be held in the Bay to assess the actual reach and possible impacts of this invasion.Finally, our ROV video transects have also underlined the significant presence of particular benthic macroalgae communities over habitat zones described as "soft-bottom" on benthic habitat maps. Although the biomass per unit area of these communities is probably lower than for most coastal rocky bottoms, this widely-spread habitat type holds a contribution to primary production to be considered in coastal ecosystem models.

Offshore benthic habitat mapping based on object-based image analysis and geomorphometric approach. A case study from the Slupsk Bank, Southern Baltic Sea

Benthic habitat mapping is a rapidly growing field of underwater remote sensing studies. This study provides the first insight for high-resolution hydroacoustic surveys in the Slupsk Bank Natura 2000 site, one of the most valuable sites in the Polish Exclusive Zone of the Southern Baltic. This study developed a quick and transparent, automatic classification workflow based on multibeam echosounder and side-scan sonar surveys to classify benthic habitats in eight study sites within the Slupsk Bank. Different predictor variables, four supervised classifiers, and the generalisation approach, improving the accuracy of the developed model were evaluated. The results suggested a very high significance for the classification performance of specific geomorphometric features that were not used in benthic habitat mapping before. These include, e.g., Fuzzy Landform Element Classification, Multiresolution Index of the Valley Bottom Flatness, and Multiresolution Index of the Ridge Top Flatness. Comparison of classification results with manual maps demonstrated that Random Forest had the highest performance of four tested supervised classifiers. Because the current needs include benthic habitat mapping for the whole area of the Polish Exclusive Economic Zone, the key findings of this study may be further applied to extensive areas in the Polish waters and other vast areas worldwide.

Integration of machine learning using hydroacoustic techniques and sediment sampling to refine substrate description in the Western Cape, South Africa

A method to map bio-physical benthic habitats on the continental shelf of South Africa has been developed and is being refined. The goal is to produce a benthic habitat classification method that bridges the disciplines of marine geophysics and biological science, with relevance to all elements of the local substrate, using modern methods. Here, we produced ground-truthed seafloor characterisation maps for two study areas (Koeberg Harbour and Clifton) in the Western Cape, South Africa. Multibeam bathymetry and backscatter data were collected and processed using machine learning clustering techniques. The study area offshore of Clifton was used to test the recently developed k-means clustering algorithm, and Koeberg Harbour, which is 35 km to the north, was used to validate the algorithm because sediment samples, along with drop camera footage, were integrated to better refine the results. Drop-camera footage was classified using the Collaborative and Automated Tools for Analysis of Marine Imagery (CATAMI) substrata classification scheme and sediment grab samples were processed using a settling tube and formulae based on the Wentworth (1922) and Folk and Ward (1957) statistics. The resulting statistics were used to define the sediment categories that were input into the clustering algorithm. The algorithm results show the distribution of sediment within the respective study areas based on the combination of inputs. Our work uses a stepwise approach from unsupervised methods (previously discussed in Pillay et al., 2020), to geological and hydroacoustic verification using ground-truthed data (discussed here), to integrated benthic habitat map production. This work focuses on the second step using hydroacoustic data and ground-truthed geological and sedimentological substrate data to create substrate maps. In the final step further hydroacoustic and biological investigations are needed in order to merge biological and geological habitats, to create benthic habitat maps, along the South African coastline.

Benthic habitat mapping from a machine learning perspective on the Cape St Francis inner shelf, Eastern Cape, South Africa

We have developed an algorithm to map benthic habitats on the continental shelf of South Africa, integrating marine geophysics and biological science. Multibeam bathymetry, backscatter and Remotely Operated Vehicle (ROV) footage were collected on the inner continental shelf of Cape St Francis and the hydroacoustic data were processed using machine learning clustering techniques. The k-means clustering algorithm was used to map the distribution of sediment at different depths. ROV footage was classified using the Collaborative and Automated Tools for Analysis of Marine Imagery (CATAMI) substrata classification scheme. Eight ROV dives along the three transects located off Seal Point, Cape St. Francis Point and within the bay were collected, and ranged from 30 to 80 m in depth. The most common Phyla in order from greatest to least abundance were; Cnidaria, Mollusca, Echinodermata, Chordata (fish species), Arthropoda (Subphylum Crustacea), Bryozoa, Porifera and Chordata (Class Ascidiacea), these were identified both on rocky substrate and sand. The first benthic habitat map of the Cape St. Francis area revealed ten different habitat types, accounting for geology, topography, and sediment cover. This work builds on an ongoing method development that incorporates broader habitat types from a range of substrates, enhancing the robustness of the algorithm, and will aid in improving our current understanding of the relationships between biota and physical habitats along the continental shelf of South Africa.

Benthic Habitat Map of the Southern Adriatic Sea (Mediterranean Sea) from Object-Based Image Analysis of Multi-Source Acoustic Backscatter Data

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.

Sentinel-2 images deliver possibilities for accurate and consistent multi-temporal benthic habitat maps in optically shallow water

The availability of frequently updated maps has always been the ultimate goal of remote sensing in benthic habitat data acquisition. Although the techniques offer cost and time effectiveness, the lack of consistency of inputted images can cause issues in successfully performing accurate multi-temporal mapping with minimum effort. This research aimed to assess the consistency of Sentinel-2 images for benthic habitat mapping in the waters of Labuan Bajo, East Nusa Tenggara, Indonesia. We employed two approaches: 1) assessing the consistency of Sentinel-2 image used to map four classes of benthic habitat from 2017 to 2019 and 2) an in-depth analysis of three images in May 2019 to assess the consistency of four-class benthic habitat mapping (coral reef, seagrass, macroalgae and bare substratum). This research incorporated atmospheric, sunglint and water column corrections and four classification algorithms: Isodata, Random Forest, Support Vector Machine, and Maximum Likelihood. The consistency of Sentinel-2 images was assessed using overall accuracy (OA), agreement percentage (AP) and total classification performance (TCP). Our results indicate that Sentinel-2 images are reliable enough for accurate benthic habitat mapping with OA >80% and consistent with agreement >80% in both approaches given the right image conditions, which are minimum cloud cover, haze, and sunglint. These factors strongly contribute to the consistency of the resulting benthic habitat classification from the Sentinel-2 images. Also, to maximize the rich image availability in the Sentinel-2 archive database, we suggest selecting images with minimal atmospheric and sunglint disturbances instead of performing image corrections that may introduce noise, leading to lower accuracy and consistency. Finally, we encourage the use of Sentinel-2 images in benthic habitat monitoring since the long-term plan of the Sentinel-2 mission guarantees the availability of these datasets.

Evaluating the Suitability of Multi-Scale Terrain Attribute Calculation Approaches for Seabed Mapping Applications

The scale dependence of benthic terrain attributes is well-accepted, and multi-scale methods are increasingly applied for benthic habitat mapping. There are, however, multiple ways to calculate terrain attributes at multiple scales, and the suitability of these approaches depends on the purpose of the analysis and data characteristics. There are currently few guidelines establishing the appropriateness of multi-scale raster calculation approaches for specific benthic habitat mapping applications. First, we identify three common purposes for calculating terrain attributes at multiple scales for benthic habitat mapping: (i) characterizing scale-specific terrain features, (ii) reducing data artefacts and errors, and (iii) reducing the mischaracterization of ground-truth data due to inaccurate sample positioning. We then define criteria that calculation approaches should fulfill to address these purposes. At two study sites, five raster terrain attributes, including measures of orientation, relative position, terrain variability, slope, and rugosity were calculated at multiple scales using four approaches to compare the suitability of the approaches for these three purposes. Results suggested that specific calculation approaches were better suited to certain tasks. A transferable parameter, termed the ‘analysis distance’, was necessary to compare attributes calculated using different approaches, and we emphasize the utility of such a parameter for facilitating the generalized comparison of terrain attributes across methods, sites, and scales.

Benthic Habitat Mapping using Sentinel 2A: A preliminary Study in Image Classification Approach in An Absence of Training Data

Numerous approaches for deriving benthic habitat mapping from visible spectrums of remotely-sensed imagery have been widely used, but image classification without training data for remote benthic habitat remains a few. In many cases, the collection of the needed ground-truth data is often prohibitively expensive or logistically infeasible. This will prevent us from providing training data for image classification purposes. In this paper, we evaluated the accuracy of the classification of benthic habitat from Sentinel 2A imagery in an absence of training data in the optically shallow water of Pari Island, Kepulauan Seribu, Indonesia. Benthic Habitat map was produced from geometrically, radiometrically, and water column corrected Sentinel 2A images. For water column correction, we performed Depth Invariant Index (DII) transformation. It was followed by the classification of Sentinel 2A imagery by applying unsupervised classification, such as IsoData and K-means algorithm. From the experiment, we produced four habitat classes. The analyses result for each unsupervised classification shows that the overall accuracy of IsoData and K-Means was 47.98% and 55.64%. However, the results of the Kappa coefficient show that the IsoData algorithm has slightly better accuracy of benthic habitat mapping (0.39) rather than K-Means (0.30).

NeMO-Net – Gamifying 3D Labeling of Multi-Modal Reference Datasets to Support Automated Marine Habitat Mapping

NASA NeMO-Net, The Neural Multimodal Observation and Training Network for global coral reef assessment, is a convolutional neural network (CNN) that generates benthic habitat maps of coral reefs and other shallow marine ecosystems. To segment and classify imagery accurately, CNNs require curated training datasets of considerable volume and accuracy. Here, we present a citizen science approach to create these training datasets through a novel 3D classification game for mobile and desktop devices. Leveraging citizen science, the NeMO-Net video game generates high-resolution 3D benthic habitat labels at the subcentimeter to meter scales. The video game trains users to accurately identify benthic categories and semantically segment 3D scenes captured using NASA airborne fluid lensing, the first remote sensing technology capable of mitigating ocean wave distortions, as well as in situ 3D photogrammetry and 2D satellite remote sensing. An active learning framework is used in the game to allow users to rate and edit other user classifications, dynamically improving segmentation accuracy. Refined and aggregated data labels from the game are used to train NeMO-Net’s supercomputer-based CNN to autonomously map shallow marine systems and augment satellite habitat mapping accuracy in these regions. We share the NeMO-Net game approach to user training and retention, outline the 3D labeling technique developed to accurately label complex coral reef imagery, and present preliminary results from over 70,000 user classifications. To overcome the inherent variability of citizen science, we analyze criteria and metrics for evaluating and filtering user data. Finally, we examine how future citizen science and machine learning approaches might benefit from label training in 3D space using an active learning framework. Within 7 months of launch, NeMO-Net has reached over 300 million people globally and directly engaged communities in coral reef mapping and conservation through ongoing scientific field campaigns, uninhibited by geography, language, or physical ability. As more user data are fed into NeMO-Net’s CNN, it will produce the first shallow-marine habitat mapping products trained on 3D subcm-scale label data and merged with m-scale satellite data that could be applied globally when data sets are available.

A benthic habitat sensitivity analysis of Qatar's coastal zone

Characterizing benthic habitat sensitivities of rapidly-developing countries is of paramount importance. Recent efforts defining the benthic habitat of Qatar's coastal zone with a high-resolution, ground-truthed benthic habitat map has provided a framework on which to develop a habitat sensitivity map. Here we present a sensitivity analysis catered towards identifying habitats with varying sensitivities to natural and anthropogenic stressors on a quantitative scale from 1 to 5, low to high. We have identified that the majority of the coastal area is low risk habitat (i.e. sandflats). However, there are six areas which are assigned as high risk by virtue of their rich occurrences of seagrass meadows, mangrove forests, and coral reefs - [1]. The reefs offshore of Al Zubarah on the west coast of the peninsula; [2] The Al Ruwais fringing reef on the northern tip of the peninsula; [3] The east-coast bays of Al Khor and Al Dhakira; [4] The shore-attached reef complex off Mesaieed (east coast); [5] The Jazirat al Bushayriyah offshore high; and, [6] The entire shelf of Halul Island. The sensitivity analysis presented here builds on a recently-developed benthic habitat map of Qatar's coastal zone and covers a total are of 4500km2. Results gleaned from this analysis can be used to support marine spatial planning objectives and ecosystem-based management decision making.

Regionalization of benthic hard-bottom communities across the Pourtalès Terrace, Florida

The Pourtalès Terrace is an exposed hard-bottom platform located south of the Florida Keys in 200–450 m depth with a diverse deep-sea coral ecosystem dominated by stylasterid hydrocorals, octocorals, and sponges that supports recreational and commercial fisheries. Portions of the Terrace have been designated as managed areas in the absence of detailed habitat maps, which hampers identifying ecological benefits derived from such management actions. Here we report analyses of historic Terrace physiographic and geologic data with more recent high-resolution bathymetric and benthic data to statistically derive a benthic community characterization across the Terrace. Multivariate analyses of faunal density from 42 standardized sites showed spatially distinct communities: East Terrace, West Terrace, Upper Terrace Edge, Sinkholes and Lophelia Coral Mound (the southernmost record of this habitat in the continental U.S.). These corresponded to physiographic divisions into an Upper Terrace comprised of Central and Karst-like regions, and Lower Terrace. A detailed description of these communities is provided. This study presents new insights into the Terrace benthic community spatial arrangement and is a necessary step towards facilitating benthic mapping. Our recommendations highlight the information needed for benthic habitat map creation and collecting data to determine if current conservation boundaries match management goals.

Distribution and extent of benthic habitats in Puck Bay (Gulf of Gdańsk, southern Baltic Sea)

The majority of the southern Baltic Sea seabed encompasses homogenous soft-bottom sediments of limited productivity and low biological diversity, but shallow productive areas in the coastal zone such as wetlands, vegetated lagoons and sheltered bays show a high variety of benthic habitat types offering favourable biotopic conditions for benthic fauna. Within Polish marine areas, semi-enclosed Puck Bay (the western part of the Gulf of Gdańsk) features an exceptionally diverse environment covering a range of benthic habitats which underscores its unique biological value and aesthetic quality and providing an impetus for conservation and ecosystem-based development. Full-coverages maps on benthic habitats in this area are therefore a necessary foundation for maritime spatial planning and implementation of strategies for sustainable management and protection of the coastal environment. This study presents the first comprehensive description and distribution of benthic habitats in Puck Bay which were categorised using the revised EUNIS 2019 classification system. Typological analyses were carried out based on inventory datasets from 1995 to 2019 including scientific publications, satellite images, open databases, topographic and geological maps, reports, theses, information available on websites and unpublished data shared willingly by individual researchers and administrative institutions. Collating various spatial data sources, that were first georeferenced and then visualized using techniques available in ArcMap 10.4.1 software (Esri), resulted in the mapping of benthic habitats and sites of important and protected plant species, which can contribute to the high confidence in environmental assessments and monitoring activities.

An Exponential Algorithm for Bottom Reflectance Retrieval in Clear Optically Shallow Waters from Multispectral Imagery without Ground Data

Bottom reflectance is a significant parameter characterizing the bottom types for clear optically shallow waters, typically in oceanic islands and reefs. However, there is not an effective physics-based method for inverting the bottom reflectance using multispectral images. In this study, we propose a novel approach for quantitatively inverting the bottom reflectance at 550 nm without the dependence of in situ bottom reflectance data or any other priori knowledge. By linking different pixels in the same image and utilizing the strong linear relationship between their water depths and the spectral related parameters, the global situation of the radiative transfer model was constrained, and an exponential relationship between the log-transformed ratio of the blue–green band reflectance and the bottom reflectance was established. The proposed model was checked by comparing the Hydrolight input bottom reflectance with that inverted from Hydrolight simulated spectrum, resulting in correlating well. Our method has successfully performed using WorldView-2 and Landsat-8 in Midway Island in the North Pacific Ocean, with the cross- and indirectly checking and obtained reliable and robust results. In addition, we assessed the potential of the quantitative bottom reflectance in benthic classification and inversion ranges under different bottom reflectance. These results indicated that compared with those methods relying on in situ data or hyperspectral imagery, our algorithm is more likely to efficiently improve the parameterization of bottom reflectance, which can be very useful for benthic habitat mapping and transferred to large-scale regions in clean reef waters, as well as monitor time-series dynamics of oceanic bottom types to forecast coral reef bleaching.