Spatial and Temporal Changes of Tidal Inlet Using Object-Based Image Analysis of Multibeam Echosounder Measurements: A Case from the Lagoon of Venice, Italy

Continuous, High-Resolution Mapping of Coastal Seafloor Sediment Distribution

Seafloor topography and grain size distribution are pivotal features in marine and coastal environments, able to influence benthic community structure and ecological processes at many spatial scales. Accordingly, there is a strong interest in multiple research disciplines to obtain seafloor geological and/or habitat maps. The aim of this study was to provide a novel, automatic and simple model to obtain high-resolution seafloor maps, using backscatter and bathymetric multibeam system data. For this purpose, we calibrated a linear regression model relating grain size distribution values, extracted from samples collected in a 16 km2 area near Bagnoli–Coroglio (southern Italy), against backscatter and depth-derived covariates. The linear model achieved excellent goodness-of-fit and predictive accuracy, yielding detailed, spatially explicit predictions of grain size. We also showed that a ground-truth sample size as large as 40% of that considered in this study was sufficient to calibrate analogous regression models in different areas. Regardless of some limitations (i.e., inability to predict rocky outcrops and/or seagrass meadows), our modeling approach proved to be a flexible tool whose main advantage is the rendering of a continuous map for sediment size, in lieu of categorical mapping approaches which usually report sharp boundaries or rely on a few sediment classes.

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.

Ensemble Mapping and Change Analysis of the Seafloor Sediment Distribution in the Sylt Outer Reef, German North Sea from 2016 to 2018

Recent studies on seafloor mapping have presented different modelling methods for the automatic classification of seafloor sediments. However, most of these studies have applied these models to seafloor data with appropriate numbers of ground-truth samples and without consideration of the imbalances in the ground-truth datasets. In this study, we aim to address these issues by conducting class-specific predictions using ensemble modelling to map seafloor sediment distributions with minimal ground-truth data combined with hydroacoustic datasets. The resulting class-specific maps were then assembled into a sediment classification map, in which the most probable class was assigned to the appropriate location. Our approach was able to predict sediment classes without bias to the class with more ground-truth data and produced reliable seafloor sediment distributions maps that can be used for seafloor monitoring. The methods presented can also be used for other underwater exploration studies with minimal ground-truth data. Sediment shifts of a heterogenous seafloor in the Sylt Outer Reef, German North Sea were also assessed to understand the sediment dynamics in the marine conservation area during two different short timescales: 2016–2018 (17 months) and 2018–2019 (4 months). The analyses of the sediment shifts showed that the western area of the Sylt Outer Reef experienced sediment fluctuations but the morphology of the bedform features was relatively stable. The results provided information on the seafloor dynamics, which can assist in the management of the marine conservation area.

Estimation of Bathymetry and Benthic Habitat Composition from Hyperspectral Remote Sensing Data (BIODIVERSITY) Using a Semi-Analytical Approach

The relevant benefits of hyperspectral sensors for water column determination and seabed features mapping compared to multispectral data, especially in coastal areas, have been demonstrated in recent studies. In this study, we used hyperspectral satellite data in the accurate mapping of the bathymetry and the composition of water habitats for inland water. Particularly, the identification of the bottom diversity for a shallow lagoon (less than 2 m in depth) was examined. Hyperspectral satellite data were simulated based on aerial hyperspectral imagery acquired above a lagoon, namely the Vaccarès lagoon (France), considering the spatial and spectral resolutions, and the signal-to-noise ratio of a satellite sensor, BIODIVERSITY, that is under study by the French space agency (CNES). Various sources of uncertainties such as inter-band calibration errors and atmospheric correction were considered to make the dataset realistic. The results were compared with a recently launched hyperspectral sensor, namely the DESIS sensor (DLR, Germany). The analysis of BIODIVERSITY-like sensor simulated data demonstrated the feasibility to satisfactorily estimate the bathymetry with a root-mean-square error of 0.28 m and a relative error of 14% between 0 and 2 m. In comparison to open coastal waters, the retrieval of bathymetry is a more challenging task for inland waters because the latter usually shows a high abundance of hydrosols (phytoplankton, SPM, and CDOM). The retrieval performance of seabed abundance was estimated through a comparison of the bottom composition with in situ data that were acquired by a recently developed imaging camera (SILIOS Technologies SA., France). Regression coefficients for the retrieval of the fractional species abundances from the theoretical inversion and measurements were obtained to be 0.77 (underwater imaging camera) and 0.80 (in situ macrophytes data), revealing the potential of the sensor characteristics. By contrast, the comparison of the in situ bathymetry and macrophyte data with the DESIS inverted data showed that depth was estimated with an RSME of 0.38 m and a relative error of 17%, and the fractional species abundance was estimated to have a regression coefficient of 0.68.

Double Trouble: Synergy between Habitat Loss and the Spread of the Alien Species Caulerpa cylindracea (Sonder) in Three Mediterranean Habitats

The role of habitat degradation on the spread of the alien green alga Caulerpa cylindracea is reported here by comparing observations achieved through a multi-year assessment on three Mediterraneans habitats, namely Posidonia oceanica meadows, Phyllophora crispa turf, and coralligenous reefs. Due to the peculiarity of the study site, both natural-reference and impacted conditions were investigated. C. cylindracea occurred in all the studied habitats under impacted conditions. High susceptibility to the invasion characterized impacted P. oceanica, where Caulerpa cover reached 70.0% in summer months. C. cylindracea cover did not differ significantly among conditions in P. crispa turf, where values never exceeded 5.0%. Conversely, the invasive green algae was low in abundance and patchily distributed in coralligenous reefs. Our results confirmed that habitat loss enhances the spread of C. cylindracea, although with different magnitudes among habitats. Dead matte areas of P. oceanica represented the most vulnerable habitat among those analyzed, whereas coralligenous reefs were less susceptible to the invasion under both the studied conditions.

A Novel Acoustic Sediment Classification Method Based on the K-Mdoids Algorithm Using Multibeam Echosounder Backscatter Intensity

The modern discrimination of sediment is based on acoustic intensity (backscatter) information from high-resolution multibeam echo-sounder systems (MBES). The backscattering intensity, varying with the angle of incidence, reveals the characteristics of seabed sediment. In this study, we propose a novel unsupervised acoustic sediment classification method based on the K-medoids algorithm using multibeam backscattering intensity data. In this method, we use the Lurton parameters model, which is the relationship between the backscattering intensity and incidence, to obtain the backscattering angle corresponding curve, and we use the genetic algorithm to fit the curve by the least-squares method. After extracting the four relevant parameters of the model when the ideal fitting effect was achieved, we input the characteristic parameters obtained from the fitting to the K-medoids clustering model. To validate the proposed classification method, we compare it with the self-organizing map (SOM) neural network classification method under the same parameter settings. The results of the experiment show that when the seabed sediment category is less than or equal to 3, the results of the K-medoids algorithm and the SOM neural network are approximately identical. As the sediment category increases, the SOM neural network shows instability, and it is impossible to see the clear boundaries of the seabed sediment, while the K-medoids category is 5 and the seabed sediment classification is correct. After comparing with field in situ seabed sediment sampling along the MBES survey line, the sediment classification method based on K-medoids is consistent with the distribution of the field sediment sampling. The classification accuracies for bedrock, sandy clay, and silty sand are all above 90%; those for gravel and clay are nearly 80%, and the overall accuracy reaches 89.7%.

Semi-Supervised Segmentation for Coastal Monitoring Seagrass Using RPA Imagery

Intertidal seagrass plays a vital role in estimating the overall health and dynamics of coastal environments due to its interaction with tidal changes. However, most seagrass habitats around the globe have been in steady decline due to human impacts, disturbing the already delicate balance in the environmental conditions that sustain seagrass. Miniaturization of multi-spectral sensors has facilitated very high resolution mapping of seagrass meadows, which significantly improves the potential for ecologists to monitor changes. In this study, two analytical approaches used for classifying intertidal seagrass habitats are compared—Object-based Image Analysis (OBIA) and Fully Convolutional Neural Networks (FCNNs). Both methods produce pixel-wise classifications in order to create segmented maps. FCNNs are an emerging set of algorithms within Deep Learning. Conversely, OBIA has been a prominent solution within this field, with many studies leveraging in-situ data and multiresolution segmentation to create habitat maps. This work demonstrates the utility of FCNNs in a semi-supervised setting to map seagrass and other coastal features from an optical drone survey conducted at Budle Bay, Northumberland, England. Semi-supervision is also an emerging field within Deep Learning that has practical benefits of achieving state of the art results using only subsets of labelled data. This is especially beneficial for remote sensing applications where in-situ data is an expensive commodity. For our results, we show that FCNNs have comparable performance with the standard OBIA method used by ecologists.

Assessment of State Transition Dynamics of Coastal Wetlands in Northern Venice Lagoon, Italy

Coastal wetlands represent particularly valuable natural resources, characterized by the interaction between their geomorphological and biological components. Their adaptation to the changing conditions depends on the rate and extent of spatial and temporal processes and their response is still not fully understood. This work aims at detecting and improving the understanding of the transition dynamics on eco-geomorphological structures in a coastal wetland ecosystem. The approach could support sustainable habitat management improving the detection and optimizing the offer of Earth Observation (EO) products for coastal system monitoring. Such course of action will strengthen evidence-based policy making, surface biophysical data sovereignty and the Space Data downstream sector through remote sensing techniques thanks to the capability of investigating larger scale and short-to-long-term dynamics. The selected case study is the Lido basin (Venice Lagoon, Italy). Our methodology offers a support in the framework of nature-based solutions, allowing the identification of ecosystem-level indicators of the surface biophysical properties influencing stability and evolution of intertidal flats on which a conceptual model is implemented. Landsat satellite imagery is used to delineate the spatial and temporal variability of the main vegetation and sediment typologies in 1990–2011. Within this period, specific anthropic activities were carried out for morphological restoration and flood protection interventions. Specifically, the lower saltmarsh shows its more fragmented part in the Baccan islet, a residual sandy spit in front of the Lido inlet. The area covered by Sarcocornia-Limonium, that triggers sediment deposition, has fluctuated yearly, from a minimum coverage of 13% to a maximum of 50%. The second decade (2001–2009) is identified as the period with major changes of halophytic and Algae-Biofilm cover typologies distribution. The power law and related thresholds, representing the patch size frequency distribution, is an indicator of the ecosystem state transition dynamics. The approach, based on multi-temporal and spatial EO analysis, is scalable elsewhere, from regional to local-to-global scale, considering the variability of climate data and anthropogenic activities. The present research also supports sustainable habitat management, improving the detection, and optimizing the offer of EO products for coastal system monitoring.

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.

Bringing Bathymetry LiDAR to Coastal Zone Assessment: A Case Study in the Southern Baltic

One of the major tasks in environmental protection is monitoring the coast for negative impacts due to climate change and anthropopressure. Remote sensing techniques are often used in studies of impact assessment. Topographic and bathymetric procedures are treated as separate measurement methods, while methods that combine coastal zone analysis with underwater impacts are rarely used in geotechnical analyses. This study presents an assessment of the bathymetry airborne system used for coastal monitoring, taking into account environmental conditions and providing a comparison with other monitoring methods. The tests were carried out on a section of the Baltic Sea where, despite successful monitoring, coastal degradation continues. This technology is able to determine the threat of coastal cliff erosion (based on the geotechnical analyses). Shallow depths have been reported to be a challenge for bathymetric Light Detection and Ranging (LiDAR), due to the difficulty in separating surface, water column and bottom reflections from each other. This challenge was overcome by describing the classification method used which was the CANUPO classification method as the most suitable for the point cloud processing. This study presents an innovative approach to identifying natural hazards, by combining analyses of coastal features with underwater factors. The main goal of this manuscript is to assess the suitability of using bathymetry scanning in the Baltic Sea to determine the factors causing coastal erosion. Furthermore, a geotechnical analysis was conducted, taking into account geometrical ground change underwater. This is the first study which uses a coastal monitoring approach, combining geotechnical computations with remote sensing data. This interdisciplinary scientific research can increase the awareness of the environmental processes.

Automatic Detection and Segmentation on Gas Plumes from Multibeam Water Column Images

The detection of gas plumes from multibeam water column (MWC) data is the most direct way to discover gas hydrate reservoirs, but current methods often have low reliability, leading to inefficient detections. Therefore, this paper proposes an automatic method for gas plume detection and segmentation by analyzing the characteristics of gas plumes in MWC images. This method is based on the AdaBoost cascade classifier, combining the Haar-like feature and Local Binary Patterns (LBP) feature. After obtaining the detected result from the above algorithm, a target localization algorithm, based on a histogram similarity calculation, is given to exactly localize the detected target boxes, by considering the differences in gas plume and background noise in the backscatter strength. On this basis, a real-time segmentation method is put forward to get the size of the detected gas plumes, by integration of the image intersection and subtraction operation. Through the shallow-water and deep-water experiment verification, the detection accuracy of this method reaches 95.8%, the precision reaches 99.35% and the recall rate reaches 82.7%. Integrated with principles and experiments, the performance of the proposed method is analyzed and discussed, and finally some conclusions are drawn.