ACOUSTIC METHODS FOR MAPPING AND CHARACTERIZING SUBMERGED AQUATIC VEGETATION USING A MULTIBEAM ECHOSOUNDER

Submerged aquatic vegetation (SAV) is an important component of many temperate global coastal ecosystems. SAV monitoring programs using optical remote sensing are limited by water clarity and attenuation with depth. Here underwater acoustics is used to analyze the water volume above the bottom to detect, map and characterize SAV. In particular, this dissertation developed and applied new methods for analyzing the full time series of acoustic intensity data (e.g., water column data) collected by a multibeam echosounder. This dissertation is composed of three separate but related studies. In the first study, novel methods for detecting and measuring the canopy height of eelgrass beds are developed and used to map eelgrass in a range of different environments throughout the Great Bay Estuary, New Hampshire, and Cape Cod Bay, Massachusetts. The results of this study validated these methods by showing agreement between boundaries of eelgrass beds in acoustic and aerial datasets more in shallow water than at the deeper edges, where the acoustics were able to detect eelgrass more easily and at lower densities. In the second study, the methods developed for measuring canopy height in the first study are used to delineate between kelp-dominated and non-kelp-dominated habitat at several shallow rocky subtidal sites on the Maine and New Hampshire coast. The kelp detection abilities of these methods are first tested and confirmed at a pilot site with detailed diver quadrat macroalgae data, and then these methods are used to successfully extrapolate kelp- and non-kelp-dominated percent coverages derived from video photomosaic data. The third study examines the variability of the acoustic signature and acoustically-derived canopy height under different tidal currents. Submerged aquatic canopies are known to bend to accommodate the drag they generate in response to hydrodynamic forcing, and, in turn, the canopy height measured by acoustics will not be a perfect representation of canopy height as defined by common seagrass monitoring protocols, which is usually measured as the length of the blade of seagrass. Additionally, the bending of the canopy affects how the blades of seagrass are distributed within the footprint of the sonar, changing the acoustic signature of the seagrass canopy. For this study, a multibeam echosounder, a current profiler and an HD video camera were deployed on a stationary frame in a single eelgrass bed over 2 tidal cycles. Acoustic canopy heights varied by as much as 30 cm over the experiment, and although acoustic canopy height was correlated to current magnitude, the relationship did not follow the predictive flexible vegetation reconfiguration model of Luhar and Nepf (2011). Results indicate that there are significant differences in the shape of the return from a deflected (i.e., bent-over) canopy and an upright canopy, and that these differences in shape have implications for the accuracy of bottom detection using the maximum amplitude of a beam time series. These three studies clearly show the potential for using multibeam water column backscatter data for mapping coastal submerged aquatic vegetation while also testing the natural variability in acoustic canopy height measurements in the field

Monitoring aquatic plants: An evaluation of hydroacoustic, on-site digitising and airborne remote sensing techniques

Aquatic macrophytes are often monitored to detect change in ecosystem function and state, as well as assessing the effectiveness of invasive aquatic plant management. This study compares seven methodologies to monitor the distribution and abundances of aquatic macrophytes. Four line transect methodologies and three spatial mapping techniques were employed in parallel over a broad turbidity gradient in two lentic habitats of south-eastern Australia. The methodologies examined included hydroacoustic surveys, on-site digitising, and digitisation of airborne remote sensing imagery. Variation in estimates of macrophyte coverage were observed between methodologies. Consistency in the collection and interpretation of data was greatest for the line transect methodologies and the digitisation of satellite imagery. Duel-frequency identification sonar proved to be an effective novel hydroacoustic technique to monitor macrophyte abundances over broad spatial scales. Single beam sonar transects was also an objective, repeatable and scalable methodology. Videography and on-site handheld PDA mapping were of limited utility due to restrictions imposed by turbidity. The utility of sidescan sonar could be improved when used in conjunction with on-site handheld PDA mapping. This study outlines important considerations when selecting a methodology to monitor macrophyte distribution and abundance. Results indicate that no one specific method can be employed across all macrophyte monitoring studies. The method or combination of methods employed during macrophyte monitoring studies is dependent upon the study objectives, budget and environmental conditions of the study site

Guidance for benthic habitat mapping: an aerial photographic approach

This document, Guidance for Benthic Habitat Mapping: An Aerial Photographic Approach, describes proven technology that can be applied in an operational manner by state-level scientists and resource managers. This information is based on the experience gained by NOAA Coastal Services Center staff and state-level cooperators in the production of a series of benthic habitat data sets in Delaware, Florida, Maine, Massachusetts, New York, Rhode Island, the Virgin Islands, and Washington, as well as during Center-sponsored workshops on coral remote sensing and seagrass and aquatic habitat assessment. (PDF contains 39 pages) The original benthic habitat document, NOAA Coastal Change Analysis Program (C-CAP): Guidance for Regional Implementation (Dobson et al.), was published by the Department of Commerce in 1995. That document summarized procedures that were to be used by scientists throughout the United States to develop consistent and reliable coastal land cover and benthic habitat information. Advances in technology and new methodologies for generating these data created the need for this updated report, which builds upon the foundation of its predecessor

Recognition and assessment of seafloor vegetation using a single beam echosounder

This study focuses on the potential of using a single beam echosounder as a tool for recognition and assessment of seafloor vegetation. Seafloor vegetation is plant benthos and occupies a large portion of the shallow coastal bottoms. It plays a key role in maintaining the ecological balance by influencing the marine and terrestrial worlds through interactions with its surrounding environment. Understanding of its existence on the seafloor is essential for environmental managers.Due to the important role of seafloor vegetation to the environment, a detailed investigation of acoustic methods that can provide effective recognition and assessment of the seafloor vegetation by using available sonar systems is necessary. One of the frequently adopted approaches to the understanding of ocean environment is through the mapping of the seafloor. Available acoustic techniques vary in kinds and are used for different purposes. Because of the wide scope of available techniques and methods which can be employed in the field, this study has limited itself to sonar techniques of normal incidence configuration relative to seafloors in selected regions and for particular marine habitats. For this study, a single beam echosounder operating at two frequencies was employed. Integrated with the echosounder was a synchronized optical system. The synchronization mechanism between the acoustic and optical systems provided capabilities to have very accurate groundtruth recordings for the acoustic data, which were then utilized as a supervised training data set for the recognition of seafloor vegetation.In this study, results acquired and conclusions made were all based on the comparison against the photographic recordings. The conclusion drawn from this investigation is only as accurate as within the selected habitat types and within very shallow water regions.In order to complete this study, detailed studies of literature and deliberately designed field experiments were carried out. Acoustic data classified with the help of the synchronized optical system were investigated by several methods. Conventional methods such as statistics and multivariate analyses were examined. Conventional methods for the recognition of the collected data gave some useful results but were found to have limited capabilities. When seeking for more robust methods, an alternative approach, Genetic Programming (GP), was tested on the same data set for comparison. Ultimately, the investigation aims to understand potential methods which can be effective in differentiating the acoustic backscatter signals of the habitats observed and subsequently distinguishing between the habitats involved in this study

Opportunities for seagrass research derived from remote sensing : a review of current methods

Seagrass communities provide critical ecosystem and provisioning services for both human populations and a wide range of associated species globally. However, it has been reported that seagrass area is decreasing at a rapid rate in many parts of the world, mostly due to anthropogenic activities including global change (pollution and climate change). The aim of this review article is to highlight the range of current tools for studying seagrasses as well as identify the benefits and limitations of a range of remote sensing and traditional methodologies. This paper provides a discussion of the ecological importance of seagrass meadows, and recent trends and developments in seagrass research methods are discussed including the use of satellite images and aerial photographs for seagrass monitoring and various image processing steps that are frequently utilised for seagrass mapping. The extensive use of various optical, Radar and LiDAR data for seagrass research in recent years has also been described in detail. The review concludes that the recent explosion of new methods and tools available from a wide range of platforms combined with the recent recognition of the importance of seagrasses provides the research community with an excellent opportunity to undertake a range of timely research. This research should include mapping the extent and distribution of seagrasses, identifying the drivers of change and factors that confer resilience, as well as quantification of the ecosystem services provided. Whilst remotely sensed data provides an important new tool it should be used in conjunction with traditional methods for validation and with a knowledge of the limitations of results and careful interpretation

Assessing Seagrass Restoration Actions through a Micro-Bathymetry Survey Approach (Italy, Mediterranean Sea)

Underwater photogrammetry provides a means of generating high-resolution products such as dense point clouds, 3D models, and orthomosaics with centimetric scale resolutions. Underwater photogrammetric models can be used to monitor the growth and expansion of benthic communities, including the assessment of the conservation status of seagrass beds and their change over time (time lapse micro-bathymetry) with OBIA classifications (Object-Based Image Analysis). However, one of the most complex aspects of underwater photogrammetry is the accuracy of the 3D models for both the horizontal and vertical components used to estimate the surfaces and volumes of biomass. In this study, a photogrammetry-based micro-bathymetry approach was applied to monitor Posidonia oceanica restoration actions. A procedure for rectifying both the horizontal and vertical elevation data was developed using soundings from high-resolution multibeam bathymetry. Furthermore, a 3D trilateration technique was also tested to collect Ground Control Points (GCPs) together with reference scale bars, both used to estimate the accuracy of the models and orthomosaics. The root mean square error (RMSE) value obtained for the horizontal planimetric measurements was 0.05 m, while the RMSE value for the depth was 0.11 m. Underwater photogrammetry, if properly applied, can provide very high-resolution and accurate models for monitoring seagrass restoration actions for ecological recovery and can be useful for other research purposes in geological and environmental monitoring

Benthic mapping of the Bluefields Bay fish sanctuary, Jamaica

Small island states, such as those in the Caribbean, are dependent on the nearshore marine ecosystem complex and its resources; the goods and services provided by seagrass and coral reef for example, are particularly indispensable to the tourism and fishing industries. In recognition of their valuable contributions and in an effort to promote sustainable use of marine resources, some nearshore areas have been designated as fish sanctuaries, as well as marine parks and protected areas. In order to effectively manage these coastal zones, a spatial basis is vital to understanding the ecological dynamics and ultimately inform management practices. However, the current extent of habitats within designated sanctuaries across Jamaica are currently unknown and owing to this, the Government of Jamaica is desirous of mapping the benthic features in these areas. Given the several habitat mapping methodologies that exist, it was deemed necessary to test the practicality of applying two remote sensing methods - optical and acoustic - at a pilot site in western Jamaica, the Bluefields Bay fish sanctuary. The optical remote sensing method involved a pixel-based supervised classification of two available multispectral images (WorldView-2 and GeoEye-1), whilst the acoustic method comprised a sonar survey using a BioSonics DT-X Portable Echosounder and subsequent indicator kriging interpolation in order to create continuous benthic surfaces. Image classification resulted in the mapping of three benthic classes, namely submerged vegetation, bare substrate and coral reef, with an overall map accuracy of 89.9% for WorldView-2 and 86.8% for GeoEye-1 imagery. These accuracies surpassed those of the acoustic classification method, which attained 76.6% accuracy for vegetation presence, and 53.5% for bottom substrate (silt, sand and coral reef/ hard bottom). Both approaches confirmed that the Bluefields Bay is dominated by submerged aquatic vegetation, with contrastingly smaller areas of bare sediment and coral reef patches. Additionally, the sonar revealed that silty substrate exists along the shoreline, whilst sand is found further offshore. Ultimately, the methods employed in this study were compared and although it was found that satellite image classification was perhaps the most cost-effective and well-suited for Jamaica given current available equipment and expertise, it is acknowledged that acoustic technology offers greater thematic detail required by a number of stakeholders and is capable of operating in turbid waters and cloud covered environments ill-suited for image classification. On the contrary, a major consideration for the acoustic classification process is the interpolation of processed data; this step gives rise to a number of potential limitations, such as those associated with the choice of interpolation algorithm, available software and expertise. The choice in mapping approach, as well as the survey design and processing steps is not an easy task; however the results of this study highlight the various benefits and shortcomings of implementing optical and acoustic classification approaches in Jamaica.Persons automatically associate tropical waters with spectacular views of coral reefs and colourful fish; however many are perhaps not aware that these coral reefs, as well as other living organisms inhabiting the seabed are in fact extremely valuable to our existence. Healthy coral reefs and seagrass assist in maintaining the sand on our beaches and fish populations and are thereby crucial to the tourism and fishing industries in the Caribbean. For this reason, a number of areas are protected by law and have been designated fish sanctuaries or marine protected areas. In order to understand the functioning of theses areas and effectively inform management strategy, the configuration of what exists on the seafloor is crucial. In the same vein that a motorist needs a road map to navigate unknown areas, coastal stakeholders require maps of the seafloor in order to understand what is happening beneath the water’s surface. The location of seafloor habitats within fish sanctuaries in Jamaica are currently unknown and the Government is interested in mapping them. However a myriad of methods exist that could be employed to achieve this goal. Remote sensing is a broad grouping of methods that involve collecting information about an object without being in direct physical contact with it. Many researchers have successfully mapped marine areas using these techniques and it was believed crucial to test the practicality of two such methods, specifically optical and acoustic remote sensing. The main question to be answered from this study was therefore: Which mapping approach is better for benthic habitat mapping in Jamaica and possibly the wider Caribbean? Optical remote sensing relates to the interaction of energy with the Earth’s surface. A digital photograph is taken from a satellite and subsequently interpreted. Acoustic/ sonar technology involves the recording of waveforms reflected from the seabed. Both methods were employed at a pilot site, the Bluefields Bay fish sanctuary, situated in western Jamaica. The optical remote sensing method involved the classification of two satellite images (named WorldView-2 and GeoEye-1) and this process was informed using known positions of seafloor features, this being known as supervised image classification. With regard to the acoustic method, a field survey utilising sonar equipment (BioSonics DT-X Portable Echosounder) was undertaken in order to collect the necessary sonar data. The processed field data was modelled in order to convert lines of field point data to one continuous map of the sanctuary, a process known as interpolation. The accuracy of each method was then tested using field knowledge of what exists in the sanctuary. The map resulting from the image classification revealed three seafloor types, namely submerged vegetation, coral reef and bare seafloor. The overall map accuracy was 89.9% for the WorldView-2 image and 86.8% for GeoEye-1 imagery. These accuracies surpassed those attained from the acoustic classification method (76.6% for vegetation presence and 53.5% for bottom type - silt, sand and coral reef/ hard bottom). Similar to previous studies undertaken, it was shown that the seabed of Bluefields Bay is primarily inhabited by submerged aquatic vegetation (including seagrass and algae), with contrastingly smaller areas of bare sediment and coral reef. Ultimately, the methods employed in this study were compared and the pros and cons of each were weighed in order to deem one method more suitable in Jamaica. Often, the presence of cloud and suspended matter in the water block the view of the seafloor making image classification difficult. On the contrary, acoustic surveys are capable of operating throughout cloudy conditions and attaining more detailed information of the ocean floor, otherwise not possible with optical remote sensing. A major step in the acoustic classification process however, was the interpolation of processed data, which may introduce additional limitations if careful consideration is not given to the intricacies of the process. Lastly, the acoustic survey certainly required greater financial resources than satellite image classification. In answer to the main question of this study, the most cost effective and feasible mapping method for Jamaica is satellite image classification (based on the results attained). It must be stressed however that the effective implementation of any method will depend on a number of factors, such as available software, equipment, expertise and user needs, that must be weighed in order to select the most feasible mapping method for a particular site

Classifying and Mapping Aquatic Vegetation in Heterogeneous Stream Ecosystems Using Visible and Multispectral UAV Imagery

The need for assessment and management of aquatic vegetation in stream ecosystems is recognized given the importance in impacting water quality, hydrodynamics, and aquatic biota. However, existing approaches to monitor are laborious and its currently not feasible to track spatial and temporal differences at broad scales. The objective of this study was therefore to map and classify aquatic vegetation of a shallow stream with heterogenous mixtures of emergent and submerged aquatic vegetation. Data was collected in the Camden Creek watershed within the Inner Bluegrass Region of central Kentucky. The use of unmanned aerial vehicles (UAVs) was employed and both visible (RGB) and multispectral imagery were collected. Machine learning techniques were applied in an off-the-shelf software (QGIS environment) to develop visible and multispectral classification land-cover maps following an effective object-based image analysis workflow. Visible images were additionally coupled with high frequency water quality data to examine the spatial and temporal behavior of the aquatic vegetation. Results showed high overall classification accuracies (OA=83.5% for the training dataset and OA=83.73% for the validation dataset) for the visible imagery, with excellent user’s and producer’s accuracies for duckweed, both for training and validation. Surprisingly, multispectral overall accuracies were substantial (OA=77.8% for the training dataset and OA=70.2% for the validation dataset) but were inferior to the visible classification results. User’s and producer’s accuracies were lower for almost all classes. However, this approach was unsuccessful in detecting, segmenting and classifying submerged aquatic vegetation (algae) for both datasets. Finally, a change detection algorithm was applied to the visible classified maps and the changes in duckweed areal coverage were successfully estimated

Quantification of submerged seagrass total aboveground biomass for Malaysian coastal waters using remote sensing data

Multi-species of seagrass forms dense benthic communities in the coastal clear (Case 1) and less clear water (Case 2) in Malaysia. There are two types of seagrass, i.e. intertidal and submerged, which can easily be found in Malaysia. Satellite remote sensing data is an effective tool to be used in many marine applications, including monitoring seagrass distribution at large area coverage. The emphasizes of this thesis is to determine the best two steps satellite-based approach for mapping submerged seagrass and quantifying aboveground biomass at Merambong area and Pulau Tinggi, Johor. Multi-platforms satellite data that has different data specifications have been used at both Case 1 (water dominated by phytoplankton) and Case 2 (water concentrated with water floating substances and sediments). The satellite data used for Merambong are GeoEye-1, Worldview-2, ALOS AVNIR-2, Landsat-8 OLI and Landsat-5 TM, while the satellite data for Pulau Tinggi are Worldview-2, ALOS AVNIR-2, Landsat-8 OLI and Landsat-5 TM. The robustness of seagrass detecting techniques, namely Depth Invariant Index (DII) and Bottom Reflectance Index (BRI) on remotely sensed data at different water clarity have been tested. Both techniques require measurement of radiance, deep-water radiance and ratio of attenuation coefficients while BRI needs few additional elements from nautical chart and tide calendar to attain information of the sea bottom depth during satellite passes. Ground truth data has intensively been collected at both study areas to validate and assess the finding of this study. Comparative assessment and analysis between both techniques revealed that BRI is best to be used on Landsat-8 OLI (93.2% user accuracy) in Case 2 water while (95.0% user accuracy) in Case 1 water to identify submerged seagrass. An empirical model has been developed to devise quantification of aboveground biomass and the temporal changes by associating insitu seagrass coverage data with BRI value on the satellite images. Submerged seagrass biomass quantification using remotely sensed data is feasible in Case 2 water at required scale and accuracy (>80%), depending on the field data sufficiency, technique and choice of satellite data. In conclusion, Landsat-8 OLI with 16-bits quantization level produces more accurate results than Worldview-2 and GeoEye-1. It is able to cover a large area of study, hence it is very useful for spatio-temporal seagrass biomass monitoring project by local policy makers and related agencies