Benthic Habitat Classification Research Articles

Machine learning for non-experts: A more accessible and simpler approach to automatic benthic habitat classification

Automating identification of benthic habitats from imagery, with Machine Learning (ML), is necessary to contribute efficiently and effectively to marine spatial planning. A promising method is to adapt pre-trained general convolutional neural networks (CNNs) to a new classification task (transfer learning). However, this is often inaccessible to a non-specialist, requiring large investments in computational resources and time (for user comprehension and model training). In this paper, we demonstrate a simpler transfer learning framework for classifying broad deep-sea benthic habitats. Specifically, we take an ‘off-the-shelf’ CNN (VGG16) and use it to extract features (pixel patterns) from benthic images (without further training). The default outputs of VGG16 are then fed in to a Support Vector Machine (SVM), a classical and simpler method than deep networks. For comparison, we also train the remaining classification layers of VGG16 using stochastic gradient descent. The discriminative power of these approaches is demonstrated on three benthic datasets (574–8353 images) from Norwegian waters; each using a unique imaging platform. Benthic habitats are broadly classified as Soft Substrate (sands, muds), Hard Substrate (gravels, cobbles and boulders) and Reef (Desmophyllum pertusum). We found that the relatively simplicity of the SVM classifier did not compromise performance. Results were competitive with the CNN classifier and consistently high, with test accuracy ranging from 0.87 to 0.95 (average = 0.9 (±0.04)) across datasets, somewhat increasing with dataset size. Impressively, these results were achieved 2.4–5× faster than CNN training and had significantly less dependency on high-specification hardware. Our suggested approach maximises conceptual and practical simplicity, representing a realistic baseline for novice users when approaching benthic habitat classification. This method has wide potential. It allows automated image grouping to aid annotation or further model selection, as well as screening of old-datasets. It is especially suited to offshore scenarios as it can provide quick, albeit crude, insights into habitat presence, allowing adaptation of sampling protocols in near real-time.

Benthic Habitat Classification Using Sentinel-2a Image With And Without Water Column Correction In Pengudang Village, Bintan Regency

Research on the mapping of benthic habitats in the waters of Pengudang Village, Bintan Regency, uses Sentinel-2A imagery. This mapping process has the potential to display the results of classification of benthic habitats in shallow waters. The purpose of this study was to assess the Maximum Likelihood Classification (MLH) algorithm model for mapping benthic habitat cover through Sentinel-2A satellite imagery using the lyzenga water column correction and without water column correction and to calculate the accuracy of the MLH classification results for benthic habitats in waters Pengudang Village. This classification uses a pixel-based image classification which is grouped into a cover class for each pixel. This classification groups into 4 benthic habitat classes, namely dead coral sand (KMP), sea grass (LM), live coral sand (PKH) and sand (PS). The results showed that the application of the lyzenga correction using the MLH algorithm in mapping benthic habitats resulted in a significant accuracy rate (80,00%), while the application without lyzenga correction using the MLH algorithm resulted in an accuracy rate (76,00%). These findings support the use of the lyzenga correction method which can increase the level of accuracy by (4,00%) with the MLH algorithm as a guide in better mapping of benthic habitat cover in Pengudang Village.

Mapping reef terrain roughness and benthic habitat complexity in the Pamegaran Island, Kepulauan Seribu National Marine Park

The formation of coral cays in the Kepulauan Seribu Marine Parks, Jakarta Special Capital Region, is the result of patch reef evolution which linked to the geology of sediment accretion, including the ecology of coral growth and competition of benthic community. This study aims to map the level of reef complexity in Pamegaran Island using Benthic Terrain Modeler. Field observation to monitor benthic communities in four stations at 3- and 5- meter depth was combined with SPOT-7 image processing to evaluate in situ rugosity, 3-dimensional bathymetry and benthic habitat classification. The results of this study revealed low degree of terrain complexity as results of habitat degradation and dominant benthic class of sand. Classification of reef benthic habitat resulted in five classes, namely sand (Sd= 126.65 ha), seagrass (Sg= 35.29 ha), bare rubble (Rb= 32.68 ha), reefs dominated by dead corals (DC=3.16 ha) and dead coral with algae (DA= 20.50 ha); with 67.21% score of overall accuracy (OA). In situ rugosity at 5 m was higher than 3 m, as result of higher coverage of live corals particularly in the east site. Similar profile was shown for rugosity index, which reflects ratio between surface to planar area and seafloor gradient along the reef slope. Finally, positive correlation between reef surface and in situ rugosity implies the importance of live coral communities in supporting complex terrain and benthic habitat in the patch reef environment.

Coral reef applications of Landsat-8: geomorphic zonation and benthic habitat mapping of Xisha Islands, China

ABSTRACT Being one of the most significant and valuable coral reef systems in the South China Sea, the Xisha Islands has undergone rapid transformation due to increasing stressors from human impacts and climate change in recent years. However, as indispensable information for coral reef monitoring and management, the detailed reef extent, geomorphic zonation, or benthic composition of the Xisha Islands is not well documented. Considering limited access to the Xisha Islands, the rapid development of optical remote sensing technology provides us with a feasible mean for coral reef observation. This study adopted a water depth substitution index – probabilistic inundation (PI) – combined with depth-invariant index (DII) to achieve reef extent exploration, geomorphologic and benthic habitat types classification with unsupervised classification algorithms based on Landsat-8 time-series satellite data. Compared with two open-access datasets, the extent of each independent reef extracted from PI exhibited higher similarity with the actual boundary conditions displayed in RGB (Red-Green-Blue) composite images from Landsat-8. Based on PI and derived slope, we obtained geomorphic zonation classification results, and similarly benthic compositions were retrieved based on PI, DII, and reflectance. The overall accuracy of geomorphic zonation and benthic habitat classification results were 72% and 86%, respectively. We also interestingly discovered that corals of the Xisha Islands may be capable of an ability to resist chronic heat stress as a growth trend of reef area after two successive stress events in 2014–2015 were observed at most reefs. The proposed mapping framework of this study provides a repeatable and flexible scheme in depicting the comprehensive situation of coral reefs at Xisha Islands based only on publicly available remote sensing data without complicated pre-set parameters, which could be easily extended to coral reef research around the world. Simultaneously, the findings also provide requisite information supporting the sustainable management and conservation of coral reef ecosystems in the Xisha Islands.

Broad-scale benthic habitat classification of the South Atlantic

Marine Spatial Planning (MSP) has become a priority for many states wanting to develop national blue economy plans and meet international obligations in response to the increasing cumulative impacts of human activities and climate change. In areas beyond national jurisdiction (ABNJ), MSP is proposed as part of a package of solutions for multi-sectoral management at the ocean basin scale. To facilitate planning, maps showing the spatial distribution of marine biological diversity are required. In areas lacking data, like the South Atlantic, environmental proxies can be used to predict these distributions. We undertook broad-scale benthic habitat classification of the South Atlantic, employing two top-down approaches spanning from national waters to ABNJ. The first was non-hierarchical and clustered groups of environmental variables prior to combination; the second was hierarchical and clustered Principal Components of environmental variables. Areas of agreement between the two approaches were identified and results compared with existing national and global classifications and published biodiversity patterns. We highlight several habitat classes we can be cautiously confident represent variation in biological diversity, such as topographic features, frontal systems and some abyssal basins. We also identify critical gaps in our knowledge of regional biogeography and advocate for collaborative effort to compile benthic species records and promote further exploration of the region to address these gaps. These insights into the distribution of habitats have the potential to support sustainable use and conservation of biodiversity beyond national jurisdiction, enable transboundary and ocean basin scale management, and empower nations to make progress towards achieving Sustainable Development Goals.

Estimation of the Benthic Habitat Zonation by Photo-Quadrat Image Analysis along the Fringing Reef of Weno Island, Chuuk, Micronesia

Benthic habitat zonation is described from in situ observations and seabed photographs taken from the coastal area of Weno Island, Chuuk, Micronesia. Habitat groups, types, and boundaries are defined by visible substratum characteristics (i.e., in situ and by digital imaging of photo-quadrats along transect lines), and by cluster and ordination analyses using relative coverage percentage of identified classification categories. The statistical similarity between habitat groups is determined by Analysis of similarity (ANOSIM). Benthic habitat groups with significant influence on the determination of habitat type are isolated by the similarity percentage (SIMPER) test. In addition to the standard practices of using transect lines and collecting data in accordance with the already well-implemented and thoroughly-tested benthic habitat classification scheme for tropical reef monitoring, we applied simple statistics that enable comparative data interpretation. Our simple, repeatable methods provide a framework for benthic habitat-related monitoring research that allows the comparison of results across regions.

Automatic Semantic Segmentation of Benthic Habitats Using Images from Towed Underwater Camera in a Complex Shallow Water Environment

Underwater image segmentation is useful for benthic habitat mapping and monitoring; however, manual annotation is time-consuming and tedious. We propose automated segmentation of benthic habitats using unsupervised semantic algorithms. Four such algorithms––Fast and Robust Fuzzy C-Means (FR), Superpixel-Based Fast Fuzzy C-Means (FF), Otsu clustering (OS), and K-means segmentation (KM)––were tested for accuracy for segmentation. Further, YCbCr and the Commission Internationale de l’Éclairage (CIE) LAB color spaces were evaluated to correct variations in image illumination and shadow effects. Benthic habitat field data from a geo-located high-resolution towed camera were used to evaluate proposed algorithms. The Shiraho study area, located off Ishigaki Island, Japan, was used, and six benthic habitats were classified. These categories were corals (Acropora and Porites), blue corals (Heliopora coerulea), brown algae, other algae, sediments, and seagrass (Thalassia hemprichii). Analysis showed that the K-means clustering algorithm yielded the highest overall accuracy. However, the differences between the KM and OS overall accuracies were statistically insignificant at the 5% level. Findings showed the importance of eliminating underwater illumination variations and outperformance of the red difference chrominance values (Cr) in the YCbCr color space for habitat segmentation. The proposed framework enhanced the automation of benthic habitat classification processes.

Benthic habitat mapping using Object-Based Image Analysis (OBIA) on Tidung Island, Kepulauan Seribu, DKI Jakarta

Tidung Island is one of the islands in Kepulauan Seribu, DKI Jakarta, Indonesia. This island has various benthic that live on the coastal areas, and benthic habitat has various functions both ecologically and economically. Nowadays, remote sensing technology is one way to detect benthic habitats in coastal areas. Mapping benthic habitat is essential for sustainable coastal resource management and to predict the distribution of benthic organisms. This study aims to map the benthic habitats using the object-based image analysis (OBIA) and calculate the accuracy of benthic habitat classification results in Tidung Island, Kepulauan Seribu, DKI Jakarta. The field data were collected on June 2021, and the image data used is satellite Sentinel-2 imagery acquired in June 2021. The result shows that the benthic habitat classification was produced in 4 classes: seagrass, rubble, sand, and live coral. The accuracy test result obtained an overall accuracy (OA) of 74.29% at the optimum value of the MRS segmentation scale 15;0,1;0.7 with the SVM algorithm. The results of benthic habitat classification show that the Seagrass class dominates the shallow water area at the research site with an area of 118.77 ha followed by Life Coral 104.809 ha, Sand 43.352 ha, and the smallest area is the Rubble class of 42.28 Ha.

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.

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.

Accuracy assessment of relative and absolute water column correction methods for benthic habitat mapping in Parang Island

The condition of benthic habitats in optically shallow sea waters becomes important information in the inventory and processing of coastal resources. Remote sensing is effective and efficient in mapping benthic habitats. This study aims to apply absolute and relative water column correction methods in order to map benthic habitats on Parang Island using PlanetScope image. The benthic habitat classification scheme used consists of coral reefs, seagrass, macroalgae, and substrate. We compared the accuracy of benthic habitat map based on absolute and relative water column correction methods. The classification methods used are the Maximum Likelihood (ML) algorithm and Support Vector Machine (SVM). The results showed that benthic habitat map with the highest accuracy was obtained by a combination of Lyzenga-ML at 61.63% followed by Purkis-SVM at 59.18%, Lyzenga-SVM at 41.90%, and Purkis-ML 16.87%. The results show that the Lyzenga water column correction method is the best choice in mapping benthic habitats.

Developing the use of convolutional neural networking in benthic habitat classification and species distribution modelling

Abstract Management of the marine environment is increasingly being conducted in accordance with an ecosystem-based approach, which requires an integrated approach to monitoring. Simultaneous acquisition of the different data types needed is often difficult, largely due to specific gear requirements (grabs, trawls, and video and acoustic approaches) and mismatches in their spatial and temporal scales. We present an example to resolve this using a convolutional neural network (CNN), using ad hoc multibeam data collected during multi-disciplinary surveys to predict the distribution of seabed habitats across the western English Channel. We adopted a habitat classification system, based on seabed morphology and sediment dynamics, and trained a CNN to label images generated from the multibeam data. The probability of the correct classification by the CNN varied per habitat, with accuracy above 60% for 85% of habitats in a training dataset. Statistical testing revealed that the spatial distribution of 57 of the 100 demersal fish and shellfish species sampled across the region during the surveys possessed a non-random relationship with the multibeam-derived habitats using CNN. CNNs, therefore, offer the potential to aid habitat mapping and facilitate species distribution modelling at the large spatial scales required under an ecosystem-based management framework.

Monitoring benthic habitats using Lyzenga model features from Landsat multi-temporal images in Google Earth Engine

Benthic habitats have conventionally been monitored through physical site visits by diving. This requires the use of expensive resources and time to achieve the desired results. This study adopts remotely sensed Landsat multi-temporal images to monitor benthic habitats within marine protected areas in Kenya. We adopt the Lyzenga approach to compute depth invariant index features on Google Earth Engine (GEE) API to aid benthic classification. Random Forest (RF) and Support Vector Machines (SVM) machine learning algorithms were used for the supervised classification of benthic habitats based on training classes selected under the guidance of an initial K-means unsupervised classification. RF had a higher overall accuracy of 83.48% compared to SVM at 52.69% as per 2015 reference data. We, therefore, used RF to map benthic habitats between 2003 and 2018. Findings indicate that there are high conversions between corals, seagrass and sand benthic classes. Since environmental and anthropogenic pressures act synergistically in causing changes, an in-depth research would be necessary to assess the vulnerability of benthic habitats to these factors. However, the use of multi-spectral imagery and powerful GEE cloud computing platform can aid monitoring and acquisition of marine data.

Analyses of inter-class spectral separability and classification accuracy of benthic habitat mapping using multispectral image

Theoretically, spectral separability will greatly affect the accuracy of multispectral classification. This study aims to understand the relationship between the inter-class spectral separability and the accuracy of benthic habitat classification using a WorldView-2 multispectral image. The study area for this research is Kemujan Island, Jepara Regency, Central Java Province, Indonesia. The datasets used are sunglint-corrected bands, Principle Component Analysis (PCA)-derived bands, vegetation indices, and filter occurrence bands. Benthic habitat field data were obtained through a photo-transect survey technique and were used to construct nine levels of benthic habitat hierarchical classification schemes. We used maximum likelihood (ML) and random forest (RF) as the classification algorithms. Spectral separability was calculated using the Jeffries-Matusita separability analysis algorithm. The results from both RF and ML showed that the increased number of class pairs with spectral separability less than 1.0 (S<1.0) decreased the OA and an increased number of class pairs with S>1.0-1.9 increased the OA. Especially for scheme Level 1 with the greatest number of classes, an increased number of class pairs with S>1.9 to is required to improve the OA. This has proven that the spectral separability between classes does affect the accuracy of benthic habitat classification and there is a significant relationship between spectral separability and the accuracy of benthic habitat classification.

Towards Benthic Habitat 3D Mapping Using Machine Learning Algorithms and Structures from Motion Photogrammetry

The accurate classification and 3D mapping of benthic habitats in coastal ecosystems are vital for developing management strategies for these valuable shallow water environments. However, both automatic and semiautomatic approaches for deriving ecologically significant information from a towed video camera system are quite limited. In the current study, we demonstrate a semiautomated framework for high-resolution benthic habitat classification and 3D mapping using Structure from Motion and Multi View Stereo (SfM-MVS) algorithms and automated machine learning classifiers. The semiautomatic classification of benthic habitats was performed using several attributes extracted automatically from labeled examples by a human annotator using raw towed video camera image data. The Bagging of Features (BOF), Hue Saturation Value (HSV), and Gray Level Co-occurrence Matrix (GLCM) methods were used to extract these attributes from 3000 images. Three machine learning classifiers (k-nearest neighbor (k-NN), support vector machine (SVM), and bagging (BAG)) were trained by using these attributes, and their outputs were assembled by the fuzzy majority voting (FMV) algorithm. The correctly classified benthic habitat images were then geo-referenced using a differential global positioning system (DGPS). Finally, SfM-MVS techniques used the resulting classified geo-referenced images to produce high spatial resolution digital terrain models and orthophoto mosaics for each category. The framework was tested for the identification and 3D mapping of seven habitats in a portion of the Shiraho area in Japan. These seven habitats were corals (Acropora and Porites), blue corals (H. coerulea), brown algae, blue algae, soft sand, hard sediments (pebble, cobble, and boulders), and seagrass. Using the FMV algorithm, we achieved an overall accuracy of 93.5% in the semiautomatic classification of the seven habitats.

Accuracy assessment of several classification algorithms with and without hue saturation intensity input features on object analyses on benthic habitat mapping in the Pajenekang Island Waters, South Sulawesi

Object-based image analysis (OBIA) method has been proven to improve the accuracy value on benthic habitat mapping. The purpose of this study was to assess the accuracy of several classification algorithms on benthic habitat mapping based on OBIA method with and without input feature of Hue Saturation Intensity (HSI) in the Pajenekang island waters, South Sulawesi, Indonesia. Sentinel-2A satellite imagery with 10 m2 spatial resolution acquired on 3 September 2018 was used in this study. During OBIA analyses, we segmented the object into 5, 10, and 15 classes and treated each of them with input features of mean+ratio vs mean+ratio+hue saturation intensity (HSI). We later classified the benthic habitat by applying several classification algorithms such as the Bayes, K-Nearest Neighbour (KNN), Support Vector Machine (SVM), and Decision Tree (DT). The results showed that the Bayes algorithm produced highest accuracy of 78.35% within 10 segmentation classes and input features of mean+ratio+HSI followed by the KNN of 71.13% with 5 segmentation classes and input features of mean+ration+HSI. The addition of HSI input features into OBIA analyses increased the accuracy of benthic habitat classification mapping of 4.13% with the Bayes classification algorithm.

Effect of the microphytobenthos photosynthesis on seabed backscattering properties

The development of hydroacoustic techniques for benthic habitat classification requires understanding of biological effects on seabed geoacoustic properties. The study addresses only one biological process that could potentially change the properties—photosynthesis of benthic microalgae. The previous investigations demonstrated that the impact could be important in warm southern marine waters of relatively high salinity. This motivated us to study the effect of the microphytobenthos photosynthesis on the backscattering properties of the sandy sediments of the southern Baltic Sea, an area of lower temperature and salinity. The five multiday laboratory experiments, different in hydrophysical or biological conditions, were conducted. The backscatter data were acquired in the small tank with sandy bottom under controlled constant temperature and salinity with simulated “day” and “night” conditions (light/dark (L/D) photocycles). Oxygen content in the water column as well as biological and biooptical parameters were additionally monitored. The diel variations of the backscattered signal energy were analyzed. The study demonstrated the impact of microphytobenthos photosynthesis on the backscattering properties of the marine sediment and its sensitivity to the abiotic (illumination level) and biotic (benthic microalgal biomass and macrozoobenthos bioturbation) factors.

The effect of atmospheric correction on object based image classification using SPOT-7 imagery: a case study in the Harapan and Kelapa Islands

Object-based classification (OBIA) has become a new paradigm in remote sensing data analysis. Errors in interpretation of benthic habitat classification image can be caused by deviation of values in reflectance. The absorption, scattering, and atmospheric reflections caused differences between the reflectance value of satellite images and real objects. The author have tried to compare two different approaches of OBIA classification with FLAASH atmospheric correction and OBIA classification without atmospheric correction. In this research, atmospheric correction with FLAASH method on SPOT-7 imagery conducted to eliminate atmospheric effect on the image. The Support Vactor Machines (SVM) algorithm is selected for the classification of benthic habitat in Harapan and Kelapa Island. Atmospheric-corrected image was analyzed visually then compared to ortho image in reflectance pattern, object segmentation and classification result. Results showed an improvement of visualization and reflectance pattern in shallow water objects. This study concluded that the FLAASH method as one of the appropriate methods to be used in atmospheric correction in SPOT-7 images.

Object-based classification of sub-bottom profiling data for benthic habitat mapping. Comparison with sidescan and RoxAnn in a Greek shallow-water habitat

Although Ground Discrimination Systems and Multi-beam Echo Sounders tend to be the preferred tools towards gaining knowledge about the seafloor, numerous surveys have been and are still being performed by simultaneously using conventional sidescan Sonars (SSSs) and Sub-Bottom Profilers (SBPs). This is in respect of gaining fast, wide-scale, three-dimensional imaging of the seafloor and its substrate, extracting maximum value from a single, time-limited survey. The combination of these two systems offers good knowledge of both the stratigraphy and the habitat extents on the seabed, aspects often linked to each other. However, a basic drawback is the inconsistency between their mapping scales, as SSS produces high-resolution backscatter maps and SBP produces substrata information of high vertical but very low horizontal density. In this work, 100-kHz SSS and 3.5-kHz SBP data, collected simultaneously during a geophysical survey at a 5.5 × 3.3 km, shallow (10–50 m depth) Marine Protected Area in Lourdas Gulf, Kefalonia Island, Greece, underwent post-processing and analysis, to extract numerous statistical features from both the seafloor and its substrate, towards automated seafloor classification. The SSS mosaic image was subjected to segmentation, coupling the mean-shift algorithm and unsupervised classification. The SBP images were processed using edge enhancement/detection techniques and numerous features were extracted regarding the acoustic appearance, transparency and density of the automatically detected seismic reflectors. Supervised classification of the SBP derivatives exhibited their high discrimination ability between different sea-bed types, making them suitable for ground discrimination. Linking of the SBP classes to the SSS segments (object-based analysis) led to full coverage, high detailed benthic habitat maps. Training of the classifiers was carried out using data points in proximity to underwater video inspected areas, while validation was performed using a manual habitat map made by expert interpreters. Three marine habitat classification schemes have been examined separately, namely NATURA, EUNIS and a custom one, successively including more Habitat types in a hierarchical manner, as to validate the method under different complexity levels. The SSS mosaic, as well as an independent data-set acquired using RoxAnn acoustic ground discrimination system, were also used, each individually, to classify Lourdas Gulf in yet again an object-based manner. The latter showed that SBP derivatives led to an equally significant and in most cases better classification of benthic habitats, compared to those produced by the widely tested and documented SSS and RoxAnn systems, implying that they deserve further exploitation.

The Camera-Based Assessment Survey System (C-BASS): A towed camera platform for reef fish abundance surveys and benthic habitat characterization in the Gulf of Mexico

An ongoing challenge for fisheries management is to provide cost-effective and timely estimates of habitat stratified fish densities. Traditional approaches use modified commercial fishing gear (such as trawls and baited hooks) that have biases in species selectivity and may also be inappropriate for deployment in some habitat types. Underwater visual and optical approaches offer the promise of more precise and less biased assessments of relative fish abundance, as well as direct estimates of absolute fish abundance. A number of video-based approaches have been developed and the technology for data acquisition, calibration, and synthesis has been developing rapidly.Beginning in 2012, our group of engineers and researchers at the University of South Florida has been working towards the goal of completing large scale, video-based surveys in the eastern Gulf of Mexico. This paper discusses design considerations and development of a towed camera system for collection of video-based data on commercially and recreationally important reef fishes and benthic habitat on the West Florida Shelf. Factors considered during development included potential habitat types to be assessed, sea-floor bathymetry, vessel support requirements, personnel requirements, and cost-effectiveness of system components. This regional-specific effort has resulted in a towed platform called the Camera-Based Assessment Survey System, or C-BASS, which has proven capable of surveying tens of kilometers of video transects per day and has the ability to cost-effective population estimates of reef fishes and coincident benthic habitat classification.