Protected yet unmanaged: insights into the ecological status of conservation priority stony reefs in Belgian waters based on the integrative use of remote sensing technologies

Stony reefs are ecologically important, providing irreplaceable ecosystem services. These fragile environments are recognised as conservation priorities by all relevant global and European policies. Bottom-contacting fisheries are an important source of anthropogenic disturbance to the seafloor’s physical and ecological integrity having immediate and destructive consequences on stony reefs and compromising ecological functions. This study, aimed to assess the ecological status (community composition and functions) of two stony reef areas -Northwest and Hinder Banks study sites -in Belgian waters using multiple remote sensing tools. Insights on the study sites’ geomorphological context and fishing patterns were gained using echo-sounding and publicly available satellite data. Video-based benthic community data were assessed against the exposure to fishing pressure using a trait-based approach linked to the biotas’ resistance and recovery potential. In the Northwest study site, between 2019 and 2022 there was a significant decline in the abundance of benthic species classified with low resistance and recovery potential to trawling. Conversely, there was a notable increase in species with moderate scores. During the same period, this site experienced an eight-fold increase in fishing effort and the hydroacoustic data revealed the presence of several trawl-marks in 2022. Similar changes in benthic communities were observed in the Hinder Banks too. Here, the abundance of species with low resistance and recovery potential was significantly lower in locations that were geomorphologically exposed to trawling compared to sheltered ones. Exposed locations had a higher abundance of opportunistic species, with moderate to high scores. The presence of several trawl marks on the seafloor was observed in the exposed locations, corresponding to fishing hotspots identified in the satellite data. Trawling activities marginally impacted richness and total abundance, but negatively altered benthic functional composition. The findings of this study provide scientific evidence of the detrimental impact of bottom-contacting fisheries on conservation priority biotopes and argues against the coexistence of such activities with Marine Protected Areas. The results of our investigation are of interest to environmental managers for the adequate implementation of environmental legislation in the face of rapid and widespread anthropogenic changes

Acoustic seafloor classification using the Weyl transform of multibeam echosounder backscatter mosaic

The use of multibeam echosounder systems (MBES) for detailed seafloor mapping is increasing at a fast pace. Due to their design, enabling continuous high-density measurements and the coregistration of seafloor’s depth and reflectivity, MBES has become a fundamental instrument in the advancing field of acoustic seafloor classification (ASC). With these data becoming available, recent seafloor mapping research focuses on the interpretation of the hydroacoustic data and automated predictive modeling of seafloor composition. While a methodological consensus on which seafloor sediment classification algorithm and routine does not exist in the scientific community, it is expected that progress will occur through the refinement of each stage of the ASC pipeline: ranging from the data acquisition to the modeling phase. This research focuses on the stage of the feature extraction; the stage wherein the spatial variables used for the classification are, in this case, derived from the MBES backscatter data. This contribution explored the sediment classification potential of a textural feature based on the recently introduced Weyl transform of 300 kHz MBES backscatter imagery acquired over a nearshore study site in Belgian Waters. The goodness of the Weyl transform textural feature for seafloor sediment classification was assessed in terms of cluster separation of Folk’s sedimentological categories (4-class scheme). Class separation potential was quantified at multiple spatial scales by cluster silhouette coefficients. Weyl features derived from MBES backscatter data were found to exhibit superior thematic class separation compared to other well-established textural features, namely: (1) First-order Statistics, (2) Gray Level Co-occurrence Matrices (GLCM), (3) Wavelet Transform and (4) Local Binary Pattern (LBP). Finally, by employing a Random Forest (RF) categorical classifier, the value of the proposed textural feature for seafloor sediment mapping was confirmed in terms of global and by-class classification accuracies, highest for models based on the backscatter Weyl features. Further tests on different backscatter datasets and sediment classification schemes are required to further elucidate the use of the Weyl transform of MBES backscatter imagery in the context of seafloor mapping

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

Evaluation of seabed mapping methods for fine-scale classification of extremely shallow benthic habitats – application to the Venice Lagoon, Italy

Recent technological developments of multibeam echosounder systems (MBES) allow mapping of benthic habitats with unprecedented detail. MBES can now be employed in extremely shallow waters, challenging data acquisition (as these instruments were often designed for deeper waters) and data interpretation (honed on datasets with resolution sometimes orders of magnitude lower). With extremely high-resolution bathymetry and colocated backscatter data, it is now possible to map the spatial distribution of fine scale benthic habitats, even identifying the acoustic signatures of single sponges. In this context, it isnecessary to understand which of the commonly used segmentation methods is best suited to account for such level of detail. At the same time, new sampling protocols for precisely georeferenced ground truth data need to be developed to validate the benthic environmental classification. This study focuses on a dataset collected in a shallow (2–10 m deep) tidal channel of the Lagoon of Venice, Italy. Using 0.05-m and 0.2-m raster grids, we compared a range of classifications, both pixel- based and object-based approaches, including manual, Maximum Likelihood Classifier, Jenks Optimization clustering, textural analysis and ObjectBased Image Analysis. Through a comprehensive and accurately geo-referenced ground truth dataset, we were able to identify five different classes of the substrate composition, including sponges, mixed submerged aquatic vegetation, mixed detritic bottom (fine and coarse) and unconsolidated bare sediment. We computed estimates of accuracy (namely Overall, User and Producer Accuracies) by cross tabulating predicted and reference instances. Overall, pixel based segmentations produced the highest accuracies and that the accuracy assessment is strongly dependent on the choice of classes for the segmentation. Tidal channels in the Venice Lagoon are extremely important in terms of habitats and sediment distribution, particularly within the context of the new tidal barrier being built. However, they had remained largely unexplored until now, because of the surveying challenges. The application of this remote sensing approach, combined with targeted sampling, opens a new perspective in the monitoring of benthic habitats in view of a knowledge-based management of natural resources in shallow coastal areas

Development of seafloor mapping strategies supporting integrated marine management: application of seafloor backscatter by multibeam echosounders.

Multibeam echosounding is indispensable for underwater monitoring, with backscatter and bathymetry data enabling Acoustic Seafloor Classification (ASC) and Change Detection (ACD). ASC is a maturing discipline, whilst ACD has remained virtually unexplored. To further develop techniques for the spatio-temporal quantification of seafloor status and dynamics, state-of-the-art hydroacoustic and ground-truth data were acquired in the Belgian Part of the North Sea and were integrated via automated classification routines. ASC research found variable predictive performance between supervised machine learning and unsupervised clustering classification. 300 kHz backscatter discrimination potential is weaker for heterogenous substrates, constraining the spatial structure and information content of the classification scheme. ACD methodologies were developed allowing the acoustic observation of signals of change and quantified the measurement’s sensitivity to environmental cyclicity, advancing the phenomenological and acoustical understanding of the dynamic environment: sources and magnitudes that are paramount for the establishment of ACD in environmental monitoring. Multi-parameter sampling datasets need collecting to fine-tune ASC, better interpret field backscatter measurements, and improve classification schemes. Novel datatypes, classifiers and predictors need further investigation, which together with knowledge of the system and emerging technologies, ranging from robotics to ecosystem modelling, paves the way for more innovative monitoring of the marine environment

Seafloor change detection using multibeam echosounder backscatter: case study on the Belgian part of the North Sea

To characterize seafloor substrate type, seabed mapping and particularly multibeam echosounding are increasingly used. Yet, the utilisation of repetitive MBES-borne backscatter surveys to monitor the environmental status of the seafloor remains limited. Often methodological frameworks are missing, and should comprise of a suite of change detection procedures, similarly to those developed in the terrestrial sciences. In this study, pre-, ensemble and post-classification approaches were tested on an eight km2 study site within a Habitat Directive Area in the Belgian part of the North Sea. In this area, gravel beds with epifaunal assemblages were observed. Flourishing of the fauna is constrained by overtopping with sand or increased turbidity levels, which could result from anthropogenic activities. Monitoring of the gravel to sand ratio was hence put forward as an indicator of good environmental status. Seven acoustic surveys were undertaken from 2004 to 2015. The methods allowed quantifying temporal trends and patterns of change of the main substrate classes identified in the study area; namely fine to medium homogenous sand, medium sand with bioclastic detritus and medium to coarse sand with gravel. Results indicated that by considering the entire study area and the entire time series, the gravel to sand ratio fluctuated, but was overall stable. Nonetheless, when only the biodiversity hotspots were considered, net losses and a gradual trend, indicative of potential smothering, was captured by ensemble and post-classification approaches respectively. Additionally, a two-dimensional morphological analysis, based on the bathymetric data, suggested a loss of profile complexity from 2004 to 2015. Causal relationships with natural and anthropogenic stressors are yet to be established. The methodologies presented and discussed are repeatable and can be applied to broad-scale geographical extents given that broad-scale time series datasets become available

Subtidal Natural Hard Substrate Quantitative Habitat Mapping: Interlinking Underwater Acoustics and Optical Imagery with Machine Learning

Subtidal natural hard substrates (SNHS) promote occupancy by rich benthic communities that provide irreplaceable and fundamental ecosystem functions, representing a global priority target for nature conservation and recognised in most European environmental legislation. However, scientifically validated methodologies for their quantitative spatial demarcation, including information on species occupancy and fine-scale environmental drivers (e.g., the effect of stone size on colonisation) are rare. This is, however, crucial information for sound ecological management. In this investigation, high-resolution (1 m) multibeam echosounder (MBES) depth and backscatter data and derivates, underwater imagery (UI) by video drop-frame, and grab sediment samples, all acquired within 32 km2 of seafloor in offshore Belgian waters, were integrated to produce a random forest (RF) spatial model, predicting the continuous distribution of the seafloor areal cover/m2 of the stones’ grain sizes promoting colonisation by sessile epilithic organisms. A semi-automated UI acquisition, processing, and analytical workflow was set up to quantitatively study the colonisation proportion of different grain sizes, identifying the colonisation potential to begin at stones with grain sizes Ø ≥ 2 cm. This parameter (i.e., % areal cover of stones Ø ≥ 2 cm/m2) was selected as the response variable for spatial predictive modelling. The model output is presented along with a protocol of error and uncertainty estimation. RF is confirmed as an accurate, versatile, and transferable mapping methodology, applicable to area-wide mapping of SNHS. UI is confirmed as an essential aid to acoustic seafloor classification, providing spatially representative numerical observations needed to carry out quantitative seafloor modelling of ecologically relevant parameters. This contribution sheds innovative insights into the ecologically relevant delineation of subtidal natural reef habitat, exploiting state-of-the-art underwater remote sensing and acoustic seafloor classification approaches