Seafloor Classification Research Articles

The Suitability of Machine-Learning Algorithms for the Automatic Acoustic Seafloor Classification of Hard Substrate Habitats in the German Bight

The automatic calculation of sediment maps from hydroacoustic data is of great importance for habitat and sediment mapping as well as monitoring tasks. For this reason, numerous papers have been published that are based on a variety of algorithms and different kinds of input data. However, the current literature lacks comparative studies that investigate the performance of different approaches in depth. Therefore, this study aims to provide recommendations for suitable approaches for the automatic classification of side-scan sonar data that can be applied by agencies and researchers. With random forests, support vector machines, and convolutional neural networks, both traditional machine-learning methods and novel deep learning techniques have been implemented to evaluate their performance regarding the classification of backscatter data from two study sites located in the Sylt Outer Reef in the German Bight. Simple statistical values, textural features, and Weyl coefficients were calculated for different patch sizes as well as levels of quantization and then utilized in the machine-learning algorithms. It is found that large image patches of 32 px size and the combined use of different feature groups lead to the best classification performances. Further, the neural network and support vector machines generated visually more appealing sediment maps than random forests, despite scoring lower overall accuracy. Based on these findings, we recommend classifying side-scan sonar data with image patches of 32 px size and 6-bit quantization either directly in neural networks or with the combined use of multiple feature groups in support vector machines.

Integrated Reconstruction of Late Quaternary Geomorphology and Sediment Dynamics of Prokljan Lake and Krka River Estuary, Croatia

The upper part of the Krka River estuary and Prokljan Lake are a specific example of a well-stratified estuarine environment in a submerged river canyon. Here, we reconstructed the geomorphological evolution of the area and classified the data gathered in the study, integrating multibeam echosounder data, backscatter echosounder data, side-scan sonar morpho-bathymetric surveys, and acoustic sub-bottom profiling, with the addition of ground-truthing and sediment analyses. This led to the successful classification of the bottom sediments using the object-based image analysis method. Additional inputs to the multibeam echosounder data improved the segmentation of the seafloor classification, geology, and morphology of the surveyed area. This study uncovered and precisely defined distinct geomorphological features, specifically submerged tufa barriers and carbonate mounds active during the Holocene warm periods, analogous to recent tufa barriers that still exist and grow in the upstream part of the Krka River. Fine-grained sediments, classified as estuarine sediments, hold more organic carbon than coarse-grained sediments sampled on barriers. A good correlation of organic carbon with silt sediments allowed the construction of a prediction map for marine sedimentary carbon in this estuarine/lake environment using multibeam echosounder data. Our findings highlight the importance of additional inputs to multibeam echosounder data to achieve the most accurate results.

Feature Space Exploration For Planning Initial Benthic AUV Surveys

Special-purpose Autonomous Underwater Vehicles (AUVs) are utilized for benthic (seafloor) surveys, where the vehicle collects optical imagery of the seafloor. Due to the small-sensor footprint of the cameras and the vast areas to be surveyed, these AUVs can not feasibly collect full coverage imagery of areas larger than a few tens of thousands of square meters. Therefore it is necessary for AUV paths to sample the surveys areas sparsely, yet effectively. Broad-scale acoustic bathymetric data are readily available over large areas and are often a useful prior of seafloor cover. As such, prior bathymetry can be used to guide AUV data collection. This research proposes methods for planning initial AUV surveys that efficiently explore a feature space representation of the bathymetry, in order to sample from a diverse set of bathymetric terrain. This will enable the AUV to visit areas that likely contain unique habitats and are representative of the entire survey site. We propose several information gathering planners that utilize a feature space exploration reward, to plan freeform paths or to optimize the placement of a survey template. The suitability of these methods to plan AUV surveys is evaluated based on the coverage of the feature space and also the ability to visit all classes of benthic habitat on the initial dive. Informative planners based on Rapidly expanding Random Trees (RRT) and Monte Carlo Tree Search (MCTS) were found to be the most effective. This is a valuable tool for AUV surveys as it increases the utility of initial dives. It also delivers a comprehensive training set to learn the relationship between acoustic bathymetry and visually derived seafloor classifications.

Implementation of an automated workflow for image-based seafloor classification with examples from manganese-nodule covered seabed areas in the Central Pacific Ocean

Mapping and monitoring of seafloor habitats are key tasks for fully understanding ocean ecosystems and resilience, which contributes towards sustainable use of ocean resources. Habitat mapping relies on seafloor classification typically based on acoustic methods, and ground truthing through direct sampling and optical imaging. With the increasing capabilities to record high-resolution underwater images, manual approaches for analyzing these images to create seafloor classifications are no longer feasible. Automated workflows have been proposed as a solution, in which algorithms assign pre-defined seafloor categories to each image. However, in order to provide consistent and repeatable analysis, these automated workflows need to address e.g., underwater illumination artefacts, variances in resolution and class-imbalances, which could bias the classification. Here, we present a generic implementation of an Automated and Integrated Seafloor Classification Workflow (AI-SCW). The workflow aims to classify the seafloor into habitat categories based on automated analysis of optical underwater images with only minimal amount of human annotations. AI-SCW incorporates laser point detection for scale determination and color normalization. It further includes semi-automatic generation of the training data set for fitting the seafloor classifier. As a case study, we applied the workflow to an example seafloor image dataset from the Belgian and German contract areas for Manganese-nodule exploration in the Pacific Ocean. Based on this, we provide seafloor classifications along the camera deployment tracks, and discuss results in the context of seafloor multibeam bathymetry. Our results show that the seafloor in the Belgian area predominantly comprises densely distributed nodules, which are intermingled with qualitatively larger-sized nodules at local elevations and within depressions. On the other hand, the German area primarily comprises nodules that only partly cover the seabed, and these occur alongside turned-over sediment (artificial seafloor) that were caused by the settling plume following a dredging experiment conducted in the area.

SWE_of_Bathymetry.m: A geomorphometric tool to automate discrimination between detachment and magmatic seafloor at slow-spreading ridges from shipboard multibeam bathymetry

The shapes and directionality of the oceanic crust at slow-spreading ridges are key to understanding its magmatic or tectonic emplacement. At slow-spreading ridges, magmatic terrain is marked by linearly fault-bounded abyssal hills, while a more tectonic emplacement termed detachment terrain is marked by long-lived detachment faults forming Oceanic Core Complexes (OCCs). However, the quantitative description of the magmatic and detachment regimes is still limited. We develop a novel geomorphometric technique to automate terrain classification based on the parameterisation of the shape, directionality, and curvature of the seafloor. The algorithm consists of two steps: (1) characterising the pattern observed in the horizontal axes by computing the horizontal eigenvalues of the slope vectors at each multibeam cells and (2) building a weight matrix derived from the computed slopes. The eccentricity of the horizontal eigenvalues defines the dipping pattern in the horizontal axes, hence the term slope-weighted eccentricity (SWE). The technique is applied through a moving window and is tested at 12.5°–15.5° N on the Mid-Atlantic Ridge (MAR), where the two distinct modes of spreading occur. The application of this novel geomorphometric technique yields results consistent with published qualitative interpretation and the distribution of seismicity observed from the peak amplitudes of the tertiary waves (T-waves) in the study area. Using the established algorithm, we found that 41% of the seafloor in our study area experienced detachment faulting (up to 28% are identified as OCCs), 25% experienced typical magmatic accretion, and a buffer zone termed extended terrain affects 34% of the seafloor, where the morphology shows a transition from detachment to magmatic spreading or vice versa. These findings provide new insights into seafloor classification based on the observed morphology and the potential to automate such mapping at other slow-spreading ridge regions.

AUTO-ADAPTIVE MULTI-LEVEL SEAFLOOR RECOGNITION AND LAND SEA CLASSIFICATION (AMSRLC) IN REEF-ISLAND ZONES USING ICESAT-2 LASER ALTIMETRY

Abstract. The world’s first photon-counting laser altimetry satellite, the Ice, Cloud and land Elevation Satellite-2 (ICESat-2), which was launched in 2018, has proven to have a certain bathymetric capability, which provides a new means for the surveying of island and reef zones. However, how to accurately extract and separate land surface signal photons, sea surface signal photons, and seafloor signal photons in these areas has not yet been resolved. In this paper, we propose a validated auto-adaptive multi-level classification algorithm (AMSRLC), which can realize automatic recognition and classification of seafloor, sea surface, and land surface photons in island and reef zones. A overlapping histogram, a slope-adaptive search ellipse, and a water depth adaptive local signal-to-noise ratio are respectively used to extract flat sea surface signals, island and reef surface signals with slope changes, and seafloor signals that weaken with water depth. The overall classification indicators OA, AA, and Kappa reached 0.993, 0.973, and 0.987 respectively. The algorithm can effectively detect various signals with high detection accuracy.

Cretaceous to Cenozoic controls on the genesis of the shelf-incising Perth Canyon; insights from a two-part geomorphology mapping approach

Perth Canyon is Australia's second largest submarine canyon, and its shelf-incising morphology contrasts with the more prolific slope-confined canyons that typify Australia's passive continental margin. The canyon has a sinuous course that extends 120 km from the shelf break (~180 m depth) to its fan at the foot of the continental slope (~4500 m). Though the canyon initiates only 50 km offshore from a major city, its genesis and geomorphic stability have not been well understood. Bathymetry data acquired in 2015 by the Schmidt Ocean Institute enabled the application of a new two-part seafloor classification approach to objectively map the complexity of the system in unprecedented detail. Part 1 used a semi-automated approach to classify the seafloor bathymetry into morphological categories, and Part 2 defined these units as geomorphological features through the interpretation of sub-bottom and seismic images, sediment samples and acoustic backscatter datasets. The resulting geomorphic map reveals an array of aggradational (cyclic steps and sediment waves), incisional (entrenched canyon floor and nick-points) and mass movement (slump and slab failures) features that for the first time provide detailed insights into the canyon's formative processes. Large faults and the Cretaceous palaeobathymetry appear to have strong influence on the canyon's planform, its depth of incision, and the distribution and types of mass failure that characterise its flanks. These data also reveal the Perth Canyon to be a predominantly relict feature; a large Late Cretaceous infilled incised valley (subaerial) beneath the canyon headwall likely initiated the canyon's development and represents its initial and most active phase. Two more infilled incised valleys are stacked above the first, and demonstrate a progressive decrease in scale, and presumably also canyon activity. Each incised valley represents lowstand incisions of the palaeo-Swan River, and their timing is linked to pronounced Late Cretaceous to Cenozoic sea level regression events, palaeoclimatic change, and onshore catchment enlargement. The disconnection of the modern Perth Canyon from the present day Swan River, and the low rates of sediment accumulation on the adjacent shelf and slope, ensure low rates of sediment supply to the canyon, and only infrequent ignition of turbidity currents. Low rates of sediment supply can similarly account for the entrenched morphology of the modern fan and only minimal headwall movement coincident with seismic events in 2018. However, additional core and bathymetry data for the lower canyon reaches are required to conclusively determine the extent of recent canyon activity.

Seafloor Classification Combining Shipboard Low-Frequency and AUV High-Frequency Acoustic Data: A Case Study of Duanqiao Hydrothermal Field, Southwest Indian Ridge

Seafloor Classification Combining Shipboard Low-Frequency and AUV High-Frequency Acoustic Data: A Case Study of Duanqiao Hydrothermal Field, Southwest Indian Ridge

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.

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.

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.

Seafloor classification using a multibeam echo sounder: A new rugosity index coupled with a pixel-based process to map Mediterranean marine habitats

Multibeam echo sounders (MBES) have been commonly used over the last decades in hydrography to perform bathymetric measurements with high precision and provide backscatter imagery of the seafloor. Although seafloor classification techniques have been recently developed using MBES backscatter imagery, few semi-automated or completely automated methods joining acoustic imagery and bathymetric data to map marine habitats exist today. In order to fill this gap, a pixel-based data treatment process was designed to provide map of marine habitats coupling the rugosity computed from bathymetry and the backscattering data. In this way, a specific rugosity index, called Bathymetric Automated Treatment for the Classification of the Seafloor (BATCLAS), was developed by computing the least square fitting on the bathymetric soundings in order to obtain a metric value of the seascape unevenness. The whole process relies on a software suite specifically created to treat and edit acoustic data from MBES, using classification algorithms based on customisable decision trees. Tested on an acoustic data set acquired along the French Mediterranean coast with a R2Sonic 2022 MBES and validated with field observations, this method allowed to detect and map with accuracy marine habitats such as complex rocky substrates, Posidonia oceanica (L.) Delile seagrass meadows and artificial structures.

Seafloor classification based on deep-sea multibeam data—Application to the Southwest Indian Ridge at 50.47°E

Seafloor classification based on multibeam data is an attractive approach due to its wide-coverage capability and relatively low cost. It has been successfully applied in shallow water, but may not work well in deep sea, particularly that with complex seafloor topography. In this paper, a method for deep-sea seafloor classification based on multibeam data is proposed. Firstly, bathymetry data are processed to obtain local slopes. Secondly, backscattering strength (BS) data from depth datagrams are corrected by calculated incident angles and insonified areas on the seafloor. Then BS data from several consecutive pings along the track are grouped into a set of small units in the across-track direction and the angular response curve of BS in each unit is extracted. Moreover, inversions of angular response curves are carried out to obtain the empirical parameter BSO based on a functional model. BSO values related to seafloor types and the standard deviations of inversion results are acquired at the same time. Finally, with the help of the prior information gained by analyzing the relationship between BSO values and ground truth, BSO values of all the units can be statistically analyzed and classified to generate a seafloor classification map. This method has been applied in a complex terrain area around the Duanqiao hydrothermal field on the Southwest Indian Ridge (SWIR) at 50.47°E, and six seafloor types have been classified according to the statistical analysis on BSO values and the visual ground truth.

Seafloor classification based on combined multibeam bathymetry and backscatter using deep convolution neural network

Seafloor classification based on combined multibeam bathymetry and backscatter using deep convolution neural network

Predicting habitat suitability of filter-feeder communities in a shallow marine environment, New Zealand

The distribution of benthic ecosystems, dominated by filter-feeding communities, is highly influenced by the seabed geomorphology. However, the spatial variation in settlement of these species is also affected by near-bottom currents and any changes in light, nutrient concentration and food quality often associated with increases of suspended sediment concentrations within the water column. Detailed predictions of the geographic distribution of filter-feeder species and a deeper understanding of the physical processes influencing their distribution patterns is key for effective management and conservation. To date, predictive distribution modelling has been derived essentially from geomorphological parameters, mainly using spatially limited observations. In this study, seabed mapping, oceanographic modelling, hydrographic records and biological observations are integrated to provide high-resolution prediction of filter-feeder habitat distribution within Queen Charlotte Sound/Tōtaranui and Tory Channel/Kura Te Au, South Island of New Zealand. The aim is to evaluate potential suitable habitat areas for filter-feeders to inform where habitat restoration management should focus efforts to recover communities such as the horse mussel (Atrina zelandica) or the green-lipped mussel (Perna canaliculus), both of which have high economic impact in New Zealand. To accomplish this, Maximum Entropy (MaxEnt) predictive modelling was used to produce Habitat Suitability (HS) maps, using geomorphological parameters and seafloor classification information. Final HS maps also incorporated oceanographic and sediment dynamic information, showing that filter-feeder habitat distribution is highly influenced by the hydrodynamics and sedimentary processes apart from the seafloor geomorphology. Filter-feeder communities inhabit quiescent areas, limited by depth, slope and sediment type; and coincide with regions presenting low near-bottom currents and low turbidity levels. Additionally, the obtained results reveal the effects of the coastal settlements and major marine traffic routes, limiting the suitable habitats to areas with less human impact. This study demonstrates that a multidisciplinary approach is crucial to better predict the spatial distribution of benthic communities, which is key to improve benthic habitat restoration and recovery assessments.

Characterisation of seafloor substrate using advanced processing of multibeam bathymetry, backscatter, and sidescan sonar in Table Bay, South Africa

A method to map seafloor substrates using machine learning, based on geophysical data including multibeam bathymetry, backscatter, and side-scan sonar, is currently being developed. Results from a case study in Table Bay, southwestern South Africa, are presented here, showing a method of physical seafloor classification that uses a number of statistical algorithms and software programs. In the first step of the process, a customised tool was created within ArcGIS using python scripting language to classify seafloor bathymetry, which can be applied to areas beyond South Africa. The tool created by the authors was based on pioneering work done by the National Oceanic and Atmospheric Administration on a benthic terrain modelling toolbox. In step two, multibeam bathymetry, backscatter and side-scan sonar data processed using Qimera, Fledermaus Geocoder Toolbox, and Navlog processing software, were classified using machine learning techniques including Decision Trees, Random Forests, and k-means clustering computer algorithms. The results from these algorithms were compared to classify the seafloor substrate distribution. Our results have allowed a comparison of advantages and disadvantages of each machine learning technique and we found that the k-means clustering techniques were the simplest to implement and understand and worked best based on their seafloor segmentation capabilities in Table Bay, with all three data sets (multibeam bathymetry, backscatter and side-scan sonar). In future research, ground-truthing methods (for example underwater video and grab samples) will be used to validate the interpreted data to create accurate seafloor substrate maps. This work provides the initial steps to develop a holistic predictive tool that classifies geophysical data into substrate maps using machine-learning techniques. The maps can be used to model biological communities and to produce benthic habitat maps for use in marine science and management.

Assessing the Repeatability of Automated Seafloor Classification Algorithms, with Application in Marine Protected Area Monitoring

The number and areal extent of marine protected areas worldwide is rapidly increasing as a result of numerous national targets that aim to see up to 30% of their waters protected by 2030. Automated seabed classification algorithms are arising as faster and objective methods to generate benthic habitat maps to monitor these areas. However, no study has yet systematically compared their repeatability. Here we aim to address that problem by comparing the repeatability of maps derived from acoustic datasets collected on consecutive days using three automated seafloor classification algorithms: (1) Random Forest (RF), (2) K–Nearest Neighbour (KNN) and (3) K means (KMEANS). The most robust and repeatable approach is then used to evaluate the change in seafloor habitats between 2012 and 2015 within the Greater Haig Fras Marine Conservation Zone, Celtic Sea, UK. Our results demonstrate that only RF and KNN provide statistically repeatable maps, with 60.3% and 47.2% agreement between consecutive days. Additionally, this study suggests that in low-relief areas, bathymetric derivatives are non-essential input parameters, while backscatter textural features, in particular Grey Level Co-occurrence Matrices, are substantially more effective in the detection of different habitats. Habitat persistence in the test area between 2012 and 2015 was 48.8%, with swapping of habitats driving the changes in 38.2% of the area. Overall, this study highlights the importance of investigating the repeatability of automated seafloor classification methods before they can be fully used in the monitoring of benthic habitats.

Classification of Seafloor Highs According to Legal Hermeneutics

AbstractThe classification of seafloor highs as submarine elevations that are natural components of the continental margin is the core exercise in the application of Article 76 of UNCLOS. Its outcome determines whether the outer limits are necessarily constrained by a 350 nautical mile distance line from the baselines, or, alternatively, whether the outer limits may extend far beyond that constraint where the outer edge of the continental margin goes beyond that distance. This paper examines in depth the constitutive criteria that govern the classification of seafloor highs as submarine elevations that are natural components of the continental margin. It is concluded that the interpretation of Article 76(6) of UNCLOS is necessarily dictated by a textual approach.

Exploration of the munition dumpsite Kolberger Heide in Kiel Bay, Germany: Example for a standardised hydroacoustic and optic monitoring approach

Post-war marine munition dumpsites do exist and are acknowledged by authorities, but their real extent and their effect onto the environment are mostly unknown. Military historic reconstruction and ocean current data (from in-situ measurements and modelled data) indicate that the German dumpsite in the Baltic Sea ‘Kolberger Heide’ is an active environment with a huge content of discarded munition material (DMM). Repeated high-resolution multibeam and underwater video surveys prove that Kolberger Heide contains more than 1,000 munition objects in the form of e.g. moored mines, ground mines, torpedoes and aerial bombs. An unsupervised seafloor classification was performed to show that corroded munition objects and proud explosives are in direct contact with the diverse local marine flora and fauna. Also the fact that the dumpsite is in close proximity to the shore in very shallow water (less than 15 m water depth) and displacement and burial of mines can be observed, demand an effective and standardised monitoring procedure. Via the combined approach of hydroacoustic and optical methods, areas can be identified, which should be prioritized when it comes to monitoring.

Sidescan sonar meets airborne and satellite remote sensing: challenges of a multi-device seafloor classification in extreme shallow water intertidal environments

Tidal ecosystems like the Wadden Sea are particularly valuable for their ecological and economic importance. Here, the natural dynamics of the abiotic and biotic processes is threatened by the human pressure, and great efforts are made on mapping and monitoring programs. Remote sensing techniques (e.g., satellite and airborne sources) are commonly used on land and intertidal areas, whereas hydroacoustic devices are deployed in the subtidal zones. The overlap of hydroacoustics (sidescan sonar) and airborne Lidar data in such sensitive transitional zone (inter- to subtidal) is rather uncommon. In order to test the limitations of both techniques in extremely shallow waters (0.7 m min, water depth) and to find the most efficient methods for the spatial classification of intertidal areas, a portion of the backbarrier tidal flat of Norderney was investigated. Lidar bathymetric data were used for extracting high resolution morphological information. Sidescan sonar mosaics were collected in two following years under contrasting weather conditions. An expert classification based on sidescan sonar backscatter intensity, seafloor texture, morphology, and surface sediment data subdivided the research area into 10 classes. The outcomes were compared with an existing RapidEye-based classification. The tested methods showed both advantages and limitations, which were discussed based on statistical analyses. Satellite and Lidar approaches were most suitable for mapping biogenic features (e.g., shellfish beds) over large areas, whereas sidescan sonar was superior for detail detection and discrimination of morpho-sedimentary regions. As an outlook, it is postulated to perform ground-truthed hydroacoustic mapping on small testing areas, and to use the obtained classification for training satellite-based classification algorithms.