Relationships between multibeam backscatter, sediment grain size and Posidonia oceanica seagrass distribution

Falco, Giovanni De; Tonielli, Renato; Martino, Gabriella Di; Innangi, Sara; Simeone, Simone; Parnum, Iain

doi:10.1016/j.csr.2010.09.006

Cited by 105 publications

(69 citation statements)

References 23 publications

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“…Although these works have primarily focused on developing models for seafloor sediment characterization, some studies have also incorporated this information for the discrimination of benthic biota [5][6][7][8][9]. Among these, terrestrial classification methods have been applied including; general clustering [8], linear discriminant analysis and principal component analysis [9,10], decision trees [5] and factor analysis [6]. With the multitude of classification approaches available there is a need to compare different algorithms to gain further insights into classifier performance when using acoustic sources.…”

Section: Introductionmentioning

confidence: 99%

“…The importance of preserving the effect of incidence angle from angular backscatter intensity to characterize seafloor types is well established [1][2][3][4]. Although these works have primarily focused on developing models for seafloor sediment characterization, some studies have also incorporated this information for the discrimination of benthic biota [5][6][7][8][9]. Among these, terrestrial classification methods have been applied including; general clustering [8], linear discriminant analysis and principal component analysis [9,10], decision trees [5] and factor analysis [6].…”

Section: Introductionmentioning

confidence: 99%

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Evaluation of Four Supervised Learning Methods for Benthic Habitat Mapping Using Backscatter from Multi-Beam Sonar

2012

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An understanding of the distribution and extent of marine habitats is essential for the implementation of ecosystem-based management strategies. Historically this had been difficult in marine environments until the advancement of acoustic sensors. This study demonstrates the applicability of supervised learning techniques for benthic habitat characterization using angular backscatter response data. With the advancement of multibeam echo-sounder (MBES) technology, full coverage datasets of physical structure over vast regions of the seafloor are now achievable. Supervised learning methods typically applied to terrestrial remote sensing provide a cost-effective approach for habitat characterization in marine systems. However the comparison of the relative performance of different classifiers using acoustic data is limited. Characterization of acoustic backscatter data from MBES using four different supervised learning methods to generate benthic habitat maps is presented. Maximum Likelihood Classifier (MLC), Quick, Unbiased, Efficient Statistical Tree (QUEST), Random Forest (RF) and Support Vector Machine (SVM) were evaluated to classify angular backscatter response into habitat classes using training data acquired from underwater video observations. Results for biota classifications indicated that SVM and RF produced the highest accuracies, followed by QUEST and MLC, respectively. The most important backscatter data were from the moderate incidence angles between 30 and 50. This study presents initial results for understanding how acoustic backscatter from MBES can be optimized for the characterization of marine benthic biological habitats.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Evaluation of Four Supervised Learning Methods for Benthic Habitat Mapping Using Backscatter from Multi-Beam Sonar

2012

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“…They used the angle cube method developed by Parnum [42] to analyze NMBS backscattering strengths. Although the angle cube method is a powerful method for visualising seafloor backscatter strength data, processing of the data can be complicated and the grid cell size is nearly 2ˆ2 m due to spatial interpolation [40][41][42]57]. In general, seagrass meadows have a complex structure including small patches that change from higher density areas to lower density ones over short distances.…”

Section: Discussionmentioning

confidence: 99%

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

Hamana

Komatsu

2016

Remote Sensing

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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 0.5ˆ0.5 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.

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“…These results show that ABS is strongly affected by sediment grain size, thus supporting the findings of previous studies. Similarly , De Falco et al (2010) reported that backscatter signal strongly affected by sediment grain size; being directly correlated to the weight percent of the coarse fraction (1000-1600 µm) and inversely correlated to weight percent of the finer sediments (16-500 µm). Goff et al (2004) did not find a significant relationship between backscatter intensity and the mean grain size of sands (> 4000 µm), although they found an inverse relationship with finer sediments (0.063 µm).…”

Section: Ann Modelsmentioning

confidence: 99%

Analyses of Turbidity and Acoustic Backscatter Signal With Artificial Neural Network for Estimation of Suspended Sediment Concentration

Meral¹

2018

Appl. Ecol. Env. Res.

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Abstract. The commonly used sampling method is restrictive for the spatial and temporal measurement of suspended sediment and requires intensive labor. These limitations and technological advances have led to methods based on sound or light scattering in water. In this study, the turbidity and acoustic backscattering signal (ABS) values were used with the aim of improving these methods with different artificial neural network (ANN) models; Multilayer Perceptron (MLP), Radial Basis Neural networks (RBNN) and General Regression Neural Network (GRNN). Measurements were taken in a vertical sediment tower for two different sediment sizes (< 50 µm and 50-100 µm) and concentrations (0.0-6.0 g L -1 ). In the results of the regression analyses, turbidity values had strong relationships with sediment concentration for both sediment size groups (R 2 = 0.937 and 0.967). Although the ABS values had a reasonable R 2 value (0.873) for the 50-100 µm group, the < 50 µm group did not produce a significant R 2 value with regression analyses. The remarkable differences were not observed among MLP, RBNN and GRNN model for this sediment size group, and the reasonable R 2 and RMSE results were not produced with any ANN model that had a single ABS input for the < 50 µm sediment group. On the other hand, for the other sediment group (50-100 µm), ABS values were used as a single input, and the highest R 2 (0.917) value was obtained with MLP model and it was improved with the turbidity input (up to R 2 = 0.999). The results show that the ANN model could be considered as an alternative method because it was applied successfully to estimate suspended sediment concentration using with turbidity and ABS under different particle size conditions.

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Relationships between multibeam backscatter, sediment grain size and Posidonia oceanica seagrass distribution

Cited by 105 publications

References 23 publications

Evaluation of Four Supervised Learning Methods for Benthic Habitat Mapping Using Backscatter from Multi-Beam Sonar

Evaluation of Four Supervised Learning Methods for Benthic Habitat Mapping Using Backscatter from Multi-Beam Sonar

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

Analyses of Turbidity and Acoustic Backscatter Signal With Artificial Neural Network for Estimation of Suspended Sediment Concentration

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