Marine Habitat Mapping Research Articles

A Combination of Remote Sensing Datasets for Coastal Marine Habitat Mapping Using Random Forest Algorithm in Pistolet Bay, Canada

Marine ecosystems serve as vital indicators of biodiversity, providing habitats for diverse flora and fauna. Canada’s extensive coastal regions encompass a rich range of marine habitats, necessitating accurate mapping techniques utilizing advanced technologies, such as remote sensing (RS). This study focused on a study area in Pistolet Bay in Newfoundland and Labrador (NL), Canada, with an area of approximately 170 km2 and depths varying between 0 and −28 m. Considering the relatively large coverage and shallow depths of water of the study area, it was decided to use airborne bathymetric Light Detection and Ranging (LiDAR) data, which used green laser pulses, to map the marine habitats in this region. Along with this LiDAR data, Remotely Operated Vehicle (ROV) footage, high-resolution multispectral drone imagery, true color Google Earth (GE) imagery, and shoreline survey data were also collected. These datasets were preprocessed and categorized into five classes of Eelgrass, Rockweed, Kelp, Other vegetation, and Non-Vegetation. A marine habitat map of the study area was generated using the features extracted from LiDAR data, such as intensity, depth, slope, and canopy height, using an object-based Random Forest (RF) algorithm. Despite multiple challenges, the resulting habitat map exhibited a commendable classification accuracy of 89%. This underscores the efficacy of the developed Artificial Intelligence (AI) model for future marine habitat mapping endeavors across the country.

Marine Habitat Mapping using Multibeam Echosounder Survey and Underwater Video Observations: A Case Study from Tioman Marine Park

In recent years, there has been an increasing trend of utilizing high-resolution multibeam echosounder (MBES) datasets and supervised classification via machine learning to create marine habitat maps. The purpose of current study was threefold: (1) to extract bathymetric and backscatter derivatives from a multibeam dataset, (2) to measure the correlation between bathymetric and backscatter derivatives, and (3) to generate a marine habitat map using the Random Forest (RF). Tioman Marine Park (TMP), which is situated Southeast China Sea. MBES surveyed area are encompassed an area of 406 km² and served as the location for the study. Based on results and analysis, fourteen (14) derivative were derived from bathymetry map and backscatter mosaic. The second step involved integrating variables and a total of 152 of habitat ground-truth data were used, derived from underwater imageries, and sediment samples, into an RF model to generate a map of the marine habitat. Based on marine habitat map, six habitat classes including sand, rock, gravel and sand, coral rubble, coral and rock, and coral were classified. The distribution of coral habitat was found to be correlated with the depth of the bathymetry in the shallow water region. Therefore, the study has reached the conclusion that the integration between MBES derivatives, ground-truth data, and RF machine learning algorithm is an effective in classifying the distribution of marine habitats, specifically the coral habitat.

The Impact of Image Enhancement and Transfer Learning Techniques on Marine Habitat Mapping

Marine habitat mapping is primarily done to monitor and preserve underwater ecosystems. Images captured in a marine environment suffer from acidification, pollutions, waste chemicals, and lighting conditions. Human beings are progressing fast in terms of technology and are also responsible for the degradations of ecosystems, both marine and land habitats. Marine biologists possess a lot of data for the underwater environment, but it is hard to analyze, and the task becomes tiresome. Automating this process would help marine biologists quickly monitor the environment and preserve it. Our research focuses on coral reef classification and two critical aspects, i.e., Image enhancement and recognition of coral reefs. Image enhancement plays an essential role in marine habitat mapping because of the environment in which images are taken. The literature contains many image enhancement techniques for underwater. The authors want to determine whether a single image enhancement technique is suitable for coral reefs. Four image enhancement techniques based on an extensive literature review are selected. We have used DenseNet-169 and MobileNet for image classification. It has been reported that DenseNet-169 has excellent results for coral reefs classification. Histogram techniques combined with DenseNet-169 for classification resulted in higher classification rates.

Marine Habitat Mapping Using Bathymetric LiDAR Data: A Case Study from Bonne Bay, Newfoundland

Marine habitats provide various benefits to the environment and humans. In this regard, an accurate marine habitat map is an important component of effective marine management. Newfoundland’s coastal area is covered by different marine habitats, which should be correctly mapped using advanced technologies, such as remote sensing methods. In this study, bathymetric Light Detection and Ranging (LiDAR) data were applied to accurately discriminate different habitat types in Bonne Bay, Newfoundland. To this end, the LiDAR intensity image was employed along with an object-based Random Forest (RF) algorithm. Two types of habitat classifications were produced: a two-class map (i.e., Vegetation and Non-Vegetation) and a five-class map (i.e., Eelgrass, Macroalgae, Rockweed, Fine Sediment, and Gravel/Cobble). It was observed that the accuracies of the produced habitat maps were reasonable considering the existing challenges, such as the error of the LiDAR data and lacking enough in situ samples for some of the classes such as macroalgae. The overall classification accuracies for the two-class and five-class maps were 87% and 80%, respectively, indicating the high capability of the developed machine learning model for future marine habitat mapping studies. The results also showed that Eelgrass, Fine Sediment, Gravel/Cobble, Macroalgae, and Rockweed cover 22.4% (3.66 km2), 51.4% (8.39 km2), 13.5% (2.21 km2), 6.9% (1.12 km2), and 5.8% (0.95 km2) of the study area, respectively.

Marine priority habitat mapping in a Mediterranean conservation area (Gyaros, South Aegean) through multi-platform marine remote sensing techniques

The aim of this study is to present the results of the first complete marine habitat mapping through marine remote sensing techniques in Gyaros Island, a remote island in the Cyclades archipelago with a great historical and ecological value. Gyaros Island is of great biological importance and, for this reason, since 2011, is part of the NATURA 2000 network and, by 2019, is characterised as a marine protected area (MPA). More than 80 km2 of seafloor area were inspected through a multi-platform marine remote sensing technique survey through two individual expeditions in 2014 and 2017. The remote sensing survey was conducted utilising side scan sonar, sub bottom profiler and multi- and single-beam echosounders. In addition to the remote sensing survey, an extensive ground truth network was established utilising a underwater towed camera and VanVeen sediment grabber. These resulted in the area being fully covered with high-quality data. Through these, a classification of the area was performed based on three distinct seafloor habitat mapping schemes (Expert, European Nature Information System and NATURA HD). The survey and the classification revealed that over 50% of the seafloor is covered by Posidonia oceanica meadows and coralligenous and other calcareous bio-concretion priority habitats. Based on the results of this work, Gyaros MPA was also established and different protections and conservation zones were set.

Overcoming the UAS limitations in the coastal environment for accurate habitat mapping

Unmanned Aerial Systems (UAS) have been recently used as a close-range remote sensing tool, providing high spatial and temporal resolutions compared to other remote sensing methods. Their abilities have constantly increased their use in many environmental studies with very satisfactory results. However, the acquisition of reliable information for accurate marine habitat mapping is still challenging due to the plethora of parameters (mostly environmental conditions) that affect the accurate depiction and delineation of habitats in aerial imagery.In this study we are proposing an efficient methodology for marine habitat mapping that includes three main steps: i) data acquisition and processing of UAS imagery, ii) Object-Based Image Analysis (OBIA) and classification of marine habitats, iii) image quality and accuracy assessment of high-resolution thematic maps. This methodology overcomes the UAS limitations in the coastal environment by identifying the optimal acquisition times through the UASea toolbox and fully exploits the potentials of UAS in environmental applications. Moreover, the OBIA workflow and classification combined with the image quality and accuracy assessment demonstrates the importance of such methods in producing very high-resolution and reliable maps of marine habitats.The proposed methodology was implemented in four coastal areas, in Greece, with different characteristics and prevalent environmental conditions, using different UAS with RGB sensors. In particular, the efficiency of the methodology was tested by acquiring data in several conditions in each site and by comparing the results as to the image quality and the classification accuracies. The results showed that the imagery acquired in the proposed acquisition times was of higher quality along with high classification accuracies. This methodology offers an efficient solution for the acquisition of high-resolution and reliable marine information and accurate habitat delineation that can be adaptable in a range of ecological and environmental studies.

Marine habitat mapping to support the use of conservation and anti-trawl structures in Kep Province, Cambodia

Abstract The marine habitats within the Kep Archipelago, Cambodia, support species of conservation importance and commercial value. Despite the area being designated a Marine Fisheries Management Area (equivalent to a Marine Protected Area locally), illegal trawling has continued to damage vulnerable habitats within the region. To augment the protection of the designated area, Conservation and Anti Trawl Structures (CATS) have been deployed locally. These structures can snare the nets of illegal trawlers and provide a hard substratum for coral colonization. A sidescan sonar survey and ground truthing campaign was used to precisely locate the 40 CATS deployed and produce maps of the important benthic habitats in the area. Due to the challenging coastal environment and minimal available infrastructure, this study used small, rechargeable or low-power (12 V), and low-cost habitat mapping equipment to map the approximate extent of several benthic habitats of conservation interest. The area and type of habitat protected by CATS has been estimated by combining the marine habitat map with the precise locations of the deployed CATS. It is hoped that this information will help inform local management decisions, such as optimizing the placement of future CATS.

Prioritizing forest management actions to benefit marine habitats in data-poor regions.

Land‐use change is considered one of the greatest human threats to marine ecosystems globally. Given limited resources for conservation, we adapted and scaled up a spatially explicit, linked land–sea decision support tool using open access global geospatial data sets and software to inform the prioritization of future forest management interventions that can have the greatest benefit on marine conservation in Vanuatu. We leveraged and compared outputs from two global marine habitat maps to prioritize land areas for forest conservation and restoration that can maximize sediment retention, water quality, and healthy coastal/marine ecosystems. By combining the outputs obtained from both marine habitat maps, we incorporated elements unique to each and provided higher confidence in our prioritization results. Regardless of marine habitat data source, prioritized areas were mostly located in watersheds on the windward side of the large high islands, exposed to higher tropical rainfall, upstream from large sections of coral reef and seagrass habitats, and thus vulnerable to human‐driven land use change. Forest protection and restoration in these areas will serve to maintain clean water and healthy, productive habitats through sediment retention, supporting the wellbeing of neighboring communities. The nationwide application of this linked land–sea tool can help managers prioritize watershed‐based management actions based on quantitative synergies and trade‐offs across terrestrial and marine ecosystems in data‐poor regions. The framework developed here will guide the implementation of ridge‐to‐reef management across the Pacific region and beyond.

UASea: A Data Acquisition Toolbox for Improving Marine Habitat Mapping

Unmanned aerial systems (UAS) are widely used in the acquisition of high-resolution information in the marine environment. Although the potential applications of UAS in marine habitat mapping are constantly increasing, many limitations need to be overcome—most of which are related to the prevalent environmental conditions—to reach efficient UAS surveys. The knowledge of the UAS limitations in marine data acquisition and the examination of the optimal flight conditions led to the development of the UASea toolbox. This study presents the UASea, a data acquisition toolbox that is developed for efficient UAS surveys in the marine environment. The UASea uses weather forecast data (i.e., wind speed, cloud cover, precipitation probability, etc.) and adaptive thresholds in a ruleset that calculates the optimal flight times in a day for the acquisition of reliable marine imagery using UAS in a given day. The toolbox provides hourly positive and negative suggestions, based on optimal or non-optimal survey conditions in a day, calculated according to the ruleset calculations. We acquired UAS images in optimal and non-optimal conditions and estimated their quality using an image quality equation. The image quality estimates are based on the criteria of sunglint presence, sea surface texture, water turbidity, and image naturalness. The overall image quality estimates were highly correlated with the suggestions of the toolbox, with a correlation coefficient of −0.84. The validation showed that 40% of the toolbox suggestions were a positive match to the images with higher quality. Therefore, we propose the optimal flight times to acquire reliable and accurate UAS imagery in the coastal environment through the UASea. The UASea contributes to proper flight planning and efficient UAS surveys by providing valuable information for mapping, monitoring, and management of the marine environment, which can be used globally in research and marine applications.

Beyond connecting the dots: A multi-scale, multi-resolution approach to marine habitat mapping

Conflicts of interests between economic and nature conservation stakeholders are increasingly common in coastal seas, inducing a growing need for evidence-based marine spatial planning. This requires accurate, high-resolution habitat maps showing the spatial distribution of benthic assemblages and enabling intersections of habitats and anthropogenic activities. However, such detailed maps are often not available because relevant biological data are scarce or poorly integrated. Instead, physiotope maps, solely based on abiotic variables, are now often used in marine spatial planning. Here, we investigated how pointwise, relatively sparse biological data can be integrated with gridded, high-resolution environmental data into informative habitat maps, using the intensively used southern North Sea as a case-study. We first conducted hierarchical clustering to identify discrete biological assemblages for three faunal groups: demersal fish, epifauna, and endobenthos. Using Random Forest models with high-resolution abiotic predictors, we then interpolated the distribution of these assemblages to high resolution grids. Finally, we quantified different anthropogenic pressures for each habitat. Habitat maps comprised a different number of habitats between faunal groups (6, 13, and 10 for demersal fish, epifauna, and endobenthos respectively) but showed similar spatial patterns for each group. Several of these ‘fauna-inclusive’ habitats resembled physiotopes, but substantial differences were also observed, especially when few (6; demersal fish) or most (13; epifauna) physiotopes were delineated. Demersal fishing and offshore wind farms (OWFs) were clearly associated with specific habitats, resulting in unequal anthropogenic pressure between different habitats. Natura-2000 areas were not specifically associated with demersal fishing, but OWFs were situated mostly inside these protected areas. We thus conclude that habitat maps derived from biological datasets that cover relevant faunal groups should be included more in ecology-inclusive marine spatial planning, instead of only using physiotope maps based on abiotic variables. This allows better balancing of nature conservation and socio-economic interests in continental shelf seas.

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

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

Combined effects of human pressures on Europe\u2019s marine ecosystems

Marine ecosystems are under high demand for human use, giving concerns about how pressures from human activities may affect their structure, function, and status. In Europe, recent developments in mapping of marine habitats and human activities now enable a coherent spatial evaluation of potential combined effects of human activities. Results indicate that combined effects from multiple human pressures are spread to 96% of the European marine area, and more specifically that combined effects from physical disturbance are spread to 86% of the coastal area and 46% of the shelf area. We compare our approach with corresponding assessments at other spatial scales and validate our results with European-scale status assessments for coastal waters. Uncertainties and development points are identified. Still, the results suggest that Europe’s seas are widely disturbed, indicating potential discrepancy between ambitions for Blue Growth and the objective of achieving good environmental status within the Marine Strategy Framework Directive.

Progress in marine habitat mapping: Concept, methods, and applications

Progress in marine habitat mapping: Concept, methods, and applications

An Integrated Field Survey and Remote Sensing Approach for Marine Habitat Mapping Along Hurghada Coast, Red Sea, Egypt

An Integrated Field Survey and Remote Sensing Approach for Marine Habitat Mapping Along Hurghada Coast, Red Sea, Egypt

Valuation of coastal ecosystem services in the Large Marine Ecosystems of Africa

The African coastline is bordered by highly valuable marine ecosystems, but the environmental degradation due to anthropogenic pressure alter the benefits that they render to people. Our paper aims at assessing the value of ecosystem services provided by mangroves, seagrass beds, coral reefs, and kelp forests present in the Large Marine Ecosystems (LMEs) of Africa. After the mapping of coastal marine habitats, our valuation relies on the transfer of value of all ecosystem services from reference monetary unit values, extracted from the literature. A habitat functionality index based on the assumption that a higher population density and a higher demographic growth rate lead to a decrease in the functionality and services of marine habitats was then defined and incorporated into the valuation. The surveyed coastal habitats cover about 117,000 km2, with seagrass beds being by far the most extensive habitat. Present all along the coasts of Africa, their surface area represents about 62% of surveyed coastal habitats, followed by the mangroves (23%), coral reefs (15%). Kelp forests are only present in the southern Benguela Current LME. We estimated the annual value of the LME's coastal ecosystem services at 814 billion USD. Coral reefs have the highest value (588 billion USD/year), followed by seagrass beds (135 billion USD/year), mangroves (91 billion USD/year), and kelp forests (0.4 billion USD/year). The results show that ecosystem services from the four coastal habitat types had the highest value in the Agulhas Current LME, representing 38% of the total value, followed by the Red Sea LME (28%) and the Somali Coastal Current LME (10%). The three LMEs of the Atlantic side represent 15% of the total estimated value. Our paper highlighted many gaps that remain to be filled in terms of mapping and ecosystem services assessment in Africa. Nonetheless, our estimated values can facilitate dialogue between decision-makers and managers, and between countries sharing the same habitats and marine resources, toward better management of these ecosystems.

UAS DATA ACQUISITION PROTOCOL FOR MARINE HABITAT MAPPING: AN ACCURACY ASSESSMENT STUDY

Abstract. Marine habitat mapping is essential for updating existing information, preserving, and protecting the marine environment. Unmanned Aerial Systems (UAS) are an important tool for monitoring and mapping coastal and marine environment because of their ability to provide very high-resolution aerial imagery.Environmental conditions have a critical role in marine mapping using UAS. This is due to the limitations of UAS surveys in coastal areas, i.e. the environmental conditions prevailing in the area. The limitations of weather and oceanographic conditions affecting the quality of marine data led to the creation of a UAS protocol for the acquisition of reliable marine information. The produced UAS Data Acquisition Protocol consists of three main categories: (i) Morphology of the study area, (ii) Environmental conditions, (iii) Flight parameters. These categories include the parameters that must be considered for marine habitat mapping.The aim of the present study is the accuracy assessment of the UAS protocol for marine habitat mapping through experimental flights. For the accuracy assessment of the UAS protocol, flights on different dates and environmental conditions were conducted, over a study area. The flight altitude was the same for all the missions, so the results were comparable. The high-resolution orthophoto maps derived from each date of the experiment were classified. The classification maps show several differences in the shape and size of the marine habitats which are directly dependent on the conditions that the habitats were mapped. A change detection comparison was conducted in pairs to examine the exact changes between the classified maps.The results emphasize the importance of the environmental conditions prevailing in an area during the mapping of marine habitats. The present study proves that the optimal flight conditions that are proposed of the UAS Data Acquisition protocol, respond to the real-world conditions and are important to be considered for an accurate and reliable mapping of the marine environment.

Applying an integrated approach to coastal marine habitat mapping in the north-western United Arab Emirates

Habitat mapping is essential for the management and conservation of coastal marine habitats. However, accurate and up-to-date habitat maps are rarely available for the marine realm. In this study, we mapped the coastal marine habitats of >400 km of coastline in the north-western United Arab Emirates (UAE) using a combination of data sources including remote sensing, extensive ground-truthing points, local expert knowledge and existing information. We delineated 17 habitats, including critical habitats for marine biodiversity such as coral reefs and mangroves, and previously unreported oyster beds and deep seagrasses. This innovative approach was able to produce a coastal marine habitat map with an overall accuracy of 77%. The approach allowed for the production of a spatial tool well-suited for the needs of environmental management and conservation in a previously data-deficient area of the United Arab Emirates.

Super Resolution by Deep Learning Improves Boulder Detection in Side Scan Sonar Backscatter Mosaics

In marine habitat mapping, a demand exists for high-resolution maps of the seafloor both for marine spatial planning and research. One topic of interest is the detection of boulders in side scan sonar backscatter mosaics of continental shelf seas. Boulders are oftentimes numerous, but encompass few pixels in backscatter mosaics. Therefore, both their automatic and manual detection is difficult. In this study, located in the German Baltic Sea, the use of super resolution by deep learning to improve the manual and automatic detection of boulders in backscatter mosaics is explored. It is found that upscaling of mosaics by a factor of 2 to 0.25 m or 0.125 m resolution increases the performance of small boulder detection and boulder density grids. Upscaling mosaics with 1.0 m pixel resolution by a factor of 4 improved performance, but the results are not sufficient for practical application. It is suggested that mosaics of 0.5 m resolution can be used to create boulder density grids in the Baltic Sea in line with current standards following upscaling.

Performance of Different Classifiers for Marine Habitat Mapping using Side Scan Sonar and Object-Based Image Analysis

Acoustic sonar techniques have been one of the successful underwater mapping alternatives for identifying the seafloor features. The integration between the technique and classification analysis can produce detail map of the seafloor. Among these sonar technologies, side-scan sonar (SSS) is one of the tools for underwater mapping that can provide high spatial resolution seafloor mosaic which is presented in greyscale level. However, before it can be used for the coral reef marine habitat mapping, it is essential to properly assess its performance and quantify the amount of information that can be extracted. The objective of this study is to determine the accuracy of habitat maps derived using side scan sonar data, Object-based Image Analysis (OBIA) and five different classifier algorithms; Support Vector Machine (SVM), Random Forest (RF), k-Nearest Neighbour (k-NN), Decision Tree, and Bayes. This study utilized side-scan sonar model Klein system 3000 which operated at 100kHz combined with video data that was conducted in shallow water (depth > 10m). First, eight (8) texture layers were derived from side scan sonar mosaic using GLCM technique. Then, the GLCM layers of texture features were reduced using Principal Component Analysis (PCA) and analysed to seek for the most contributed texture layers. A total of 80 samples were derived which consist of four (4) classes; coral, sand, silt and mud. The result shows that the Support Vector Machine (SVM) method produced the highest accuracy which is 81.25% followed by k-Nearest Neighbours (k-NN), Random Forest (RF), Decision Tree and Bayes (68.75%, 66.25%, 57.5% and 45% respectively). The used of OBIA with SSS data offers a promising method to map marine habitats for a better understanding of spatial distribution and monitoring habitat changes in the future.

Automated Filtering of Multibeam Water-Column Data to Detect Relative Abundance of Giant Kelp (Macrocystis pyrifera)

Modern multibeam echosounders can record backscatter data returned from the water above the seafloor. These water-column data can potentially be used to detect and map aquatic vegetation such as kelp, and thus contribute to improving marine habitat mapping. However, the strong sidelobe interference noise that typically contaminates water-column data is a major obstacle to the detection of targets lying close to the seabed, such as aquatic vegetation. This article presents an algorithm to filter the noise and artefacts due to interference from the sidelobes of the receive array by normalizing the slant-range signal in each ping. To evaluate the potential of the filtered data for the detection of aquatic vegetation, we acquired a comprehensive water-column dataset over a controlled experimental site. The experimental site was a transplanted patch of giant kelp (Macrocystis pyrifera) forest of known biomass and spatial configuration, obtained by harvesting several individuals from a nearby forest, measuring and weighing them, and arranging them manually on an area of seafloor previously bare. The water-column dataset was acquired with a Kongsberg EM 2040 C multibeam echosounder at several frequencies (200, 300, and 400 kHz) and pulse lengths (25, 50, and 100 μs). The data acquisition process was repeated after removing half of the plants, to simulate a thinner forest. The giant kelp plants produced evident echoes in the water-column data at all settings. The slant-range signal normalization filter greatly improved the visual quality of the data, but the filtered data may under-represent the true amount of acoustic energy in the water column. Nonetheless, the overall acoustic backscatter measured after filtering was significantly lower, by 2 to 4 dB on average, for data acquired over the thinned forest compared to the original experiment. We discuss the implications of these results for the potential use of multibeam echosounder water-column data in marine habitat mapping.