Acoustic Approach to Determining Seabed Substrates Distribution at Mandi Darah Island, Sabah

Marine ecosystems and natural habitat play the important role of the Earth’s life support system. They significantly contribute to economies and food safety and help preserve ecological processes. However, the devastation of the marine ecosystem in Malaysia due to the human factor and climate change is quite alarming. Therefore, spatial marine information, especially on the distribution of seabed substrates and habitat mapping, are of utmost importance for marine ecosystem management and conservation. Traditionally, seabed substrate and habitat mapping were classified based on direct observation techniques such as photography, video, sampling, coring and scuba diving. These techniques are often limited due to water clarity and weather conditions and only suitable for smaller scale surveys. In this study, we employed an acoustic approach using the RoxAnn Acoustic Ground Discrimination System (AGDS) with a high-frequency single-beam echo sounder to examine the distribution of seabed substrate at the Mandi Darah Island, Sabah. The acoustic signals recorded by AGDS are translated into hardness and roughness indices which are then used to identify the unique characteristics of the recorded seabed types. The analysis has shown that fifteen types of substrates, ranging from silt to rough/some seagrass, have been identified and classified. The findings demonstrated that the acoustic method was a better alternative for seabed substrate determination than the conventional direct observation techniques in terms of cost and time spent, especially in large scale surveys. The seabed substrate dataset from this study could be used as baseline information for the better management and conservation of the marine ecosystem

An expeditious low-cost method for the acoustic characterization of seabeds in a Mediterranean coastal protected area

Financiado para publicación en acceso aberto: Universidade de Vigo/CISUGPosidonia oceanica meadows are ecosystem engineers which, despite their ecological relevance, are experiencing habitat fragmentation and area decrease. Cartography and information on the ecological status of these habitats is key to an effective maritime spatial planning and management for habitat conservation. In this work we apply an acoustic methodology to map benthic habitats (substrate and vegetation) in an archipelago of the Natura 2000 Network close to the coast of Murcia (SE Spain) where dense and sparse areas of P. oceanica, and patches of Cymodocea nodosa appear over a sandy and had bottom. The methodology uses dual frequency information (200 kHz and 38 kHz) acquired with a single-beam echosounder to develop a bathymetry, and performs sea bottom and vegetation supervised classifications, using video and scuba diver observations as groundtruthing data. Sea bottom was classified from acoustic features of the first and second 200 kHz echoes into 5 substrate classes using a random forest classifier: sand, fine sand, coarse sand, hard bottoms and hard bottoms with sandy patches. The vegetation was classified from features extracted from the "above-bottom" part of the echo (height and backscattering intensity) in both frequencies, resulting also in a 5 class classification: C. nodosa meadows, dense P. oceanica meadows, dispersed P. oceanica meadows, dense P. oceanica with sand patches, and no-vegetation; according to the random-forest Gini index, 38 kHz features were the most informational variables for this classification. The validation accuracies of both classifications were 85% (substrates) and 70% (vegetation), close to accuracies reported in the literature when using a similar number of classes. The results of this article (including bathymetric, and substrate and vegetation thematic maps), together with the acoustic methodology described and used, are contributions that can improve the continuous monitoring of Mediterranean seagrasses

Seafloor mapping using multibeam sonar

Seafloor habitat and its marine community have greatly affected by anthropogenic pressures from various human activities. Efforts to conserve and manage the marine habitat are challenging due to the difficulty to get the details of the seafloor data. Attention has been focused towards the multibeam echo sounder system (MBES), a tool in mapping the seafloor habitats, due to its ability to produce a detailed seafloor map. The aim of this study is to utilize MBES output, namely the bathymetry, backscatter, and its derivatives in order to produce a seafloor habitat map using automated classification technique in Malaysian water. The objectives are: (i) to investigate the correlation between MBES backscatter image and signal-based method for seafloor sediment classification; (ii) to evaluate the importance of bathymetry and its derivatives in producing coral reef classification map; (iii) to perform automated technique in producing the coral reef classification map, and finally (iv) to assess the accuracy of the coral reef classification maps constructed from the techniques above. The study was conducted in two different locations: Sembilan Island, Perak and Tawau, Sabah. The results of the data reduction analysis using the Principal Component Analysis (PCA), Linear Pearson Correlation, and variable importance analysis showed four most significant derivative layers for the production of coral reef classification map were identified: (i) bathymetry, (ii) benthic position index (BPI), (iii) slope, and (iv) grey level co-occurrence matrices (GLCM) mean. The classification map constructed with the selected MBES derivatives using four different techniques (Support Vector Machine, Neural Network, QUEST decision trees, and CRUISE decision trees) had shown an encouraging results with two classifiers achieved the accuracy of more than 70% (Support Vector Machine with 73.61% and Neural Network with 70.14%). In sum, this classification seafloor habitat map has enhanced coral reef spatial distribution information, and this finding has an important contribution to the seafloor habitat mapping in Malaysia

Real-Time Classification of Seagrass Meadows on Flat Bottom with Bathymetric Data Measured by a Narrow Multibeam Sonar System

Seagrass meadows, one of the most important habitats for many marine species, provide essential ecological services. Thus, society must conserve seagrass beds as part of their sustainable development efforts. Conserving these ecosystems requires information on seagrass distribution and relative abundance, and an efficient, accurate monitoring system. Although narrow multibeam sonar systems (NMBSs) are highly effective in resolving seagrass beds, post-processing methods are required to extract key data. The purpose of this study was to develop a simple method capable of detecting seagrass meadows and estimating their relative abundance in real time using an NMBS. Because most seagrass meadows grow on sandy seafloors, we proposed a way of discriminating seagrass meadows from the sand bed. We classify meadows into three categories of relative seagrass abundance using the 95% confidence level of beam depths and the depth range of the beam depth. These are respectively two times the standard deviation of beam depths, and the difference between the shallowest and the deepest depths in a 0.5 × 0.5 m grid cell sampled with several narrow beams. We examined Zostera caulescens Miki, but this simple NMBS method of seagrass classification can potentially be used to map seagrass meadows with longer shoots of other species, such as Posidonia, as both have gas filled cavities

Modelling seagrass blue carbon stock in seagrass-mangrove habitats using remote sensing approach

Modelling seagrass blue carbon stocks are essential to complement the satellitebased remote sensing in detecting the underground seagrass carbon stocks. The green carbon initiatives have for long reported the detailed mapping and estimation procedural as well as the audit protocol of the global terrestrial carbon stocks. Research on the blue carbon mapping and its related modelling and estimation, on the other hand, is rarely if ever published as part of its importance is realised but remained scattered. Therefore, this study aimed at investigating blue carbon stocks in seagrass habitats by estimating the total carbon stored in seagrass using the satellite-based technique. The specific objectives are to : 1) assess and adapt some selected models for deriving seagrass total above-ground carbon (STAGC); 2) formulate new approach based-on selected models to combine with in-situ data, to model and estimate blue carbon stocks from seagrass total below-ground carbon (STBGC); 3) develop a novel technique using the selected models with soil organic carbon (SOC) to model and estimate the blue carbon stocks from seagrass total soil organic carbon (STSOC); and 4) integrate all the models (STAGC, STBGC, and STSOC) to produce a framework for the mapping and estimation of seagrass total blue carbon stock (STBCS). Suitable logistic functions were selected and applied on the satellite images to investigate seagrass, and soil carbon stocks along the seagrass meadows of Peninsular Malaysia (PM) coastline All the Landsat ETM+’s shortwave visible bands (blue, green, red) were employed for detecting and mapping seagrass stocks boundary within the coastline of PM. The derivation of STAGC was adopted from the existing bottom reflectance index (BRI) based technique via establishing a strong relationship between BRI with seagrass total aboveground biomass (STAGB). While for STBGC estimation, the STAGB^ (STAGB obtained from BRI image) were correlated with seagrass total below-ground biomass derived from insitu measurement (STBGB^^ro). Both these STAGB^ and STBGB^.^ro were converted into STAGC and STBGC using a conversion factor. Furthermore, the derivation of seagrass total soil organic carbon derived via laboratory test (STSOCi^b) was achieved through correlating BRI values with corresponding in-situ samples of soil organic carbon (SOC) obtained from the laboratory analysis by the Carbon-Hydrogen Nitrogen Sulphur (CHNS) analyser. These models were generated from the three major sample areas (Johor, Penang, and Terengganu), which were used to estimate the entire seagrass carbon stocks in the coastline of PM. The models revealed a robust correlation results for BRI versus STAGB (R2 = 0.962, p< 0.001), STAGB^, versus STBGB/A,wro (R2 = 0.933, p< 0.001,), and BRI and STSOC (R2 = 0 .989, p< 0.001) respectively. The STBCS for the whole seagrass meadows along the coastline of PM was finally realised, demonstrating a good agreement in accuracy assessment (Root Mean Square Error (RMSE) = +- <1MtC/ha\). It is, therefore, concluded that the new approach introduced by this research on STBGC and STSOC estimation was tested and proved significant on the entire STBCS quantification for the PM coastline. The contributions are critical to fast-track the United Nations Framework Convention on Climate Change (UNFCCC) agreement to report the STBCS contents. Hence, this study has managed to propose a new fundamental initiative for estimating STBCS for speedy realisation of 2020 agenda on targets 14.2 and 14.5 of United Nations’ Sustainable Development Goal 14th (life below the water)

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