Remote identification of a shipwreck site from MBES backscatter

Masetti, Giuseppe; Calder, Brian R.

doi:10.1016/j.jenvman.2012.06.037

Cited by 15 publications

(7 citation statements)

References 43 publications

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“…Like the bathymetry derivatives, the three HSI layers were selected based on their ability to produce accurate benthic habitat maps in previous studies [25]–[27]. Homogeneity, Entropy and Correlation were selected, among a wide range of other texture features available [44], based on their reported importance in previous texture-based habitat mapping efforts [14], [28], [45], [46] and on their belonging to three different groups, so as to minimise risks of correlation [47]. The three texture features were obtained by calculating the GLCMs in the 0°, 45°, 90° and 135° directions over the 8-bit backscatter mosaic (with no greyscale normalisation applied), extracting the features from each GLCM direction, and averaging the results.…”

Section: Methodsmentioning

confidence: 99%

Integrating Multibeam Backscatter Angular Response, Mosaic and Bathymetry Data for Benthic Habitat Mapping

et al. 2014

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Multibeam echosounders (MBES) are increasingly becoming the tool of choice for marine habitat mapping applications. In turn, the rapid expansion of habitat mapping studies has resulted in a need for automated classification techniques to efficiently map benthic habitats, assess confidence in model outputs, and evaluate the importance of variables driving the patterns observed. The benthic habitat characterisation process often involves the analysis of MBES bathymetry, backscatter mosaic or angular response with observation data providing ground truth. However, studies that make use of the full range of MBES outputs within a single classification process are limited. We present an approach that integrates backscatter angular response with MBES bathymetry, backscatter mosaic and their derivatives in a classification process using a Random Forests (RF) machine-learning algorithm to predict the distribution of benthic biological habitats. This approach includes a method of deriving statistical features from backscatter angular response curves created from MBES data collated within homogeneous regions of a backscatter mosaic. Using the RF algorithm we assess the relative importance of each variable in order to optimise the classification process and simplify models applied. The results showed that the inclusion of the angular response features in the classification process improved the accuracy of the final habitat maps from 88.5% to 93.6%. The RF algorithm identified bathymetry and the angular response mean as the two most important predictors. However, the highest classification rates were only obtained after incorporating additional features derived from bathymetry and the backscatter mosaic. The angular response features were found to be more important to the classification process compared to the backscatter mosaic features. This analysis indicates that integrating angular response information with bathymetry and the backscatter mosaic, along with their derivatives, constitutes an important improvement for studying the distribution of benthic habitats, which is necessary for effective marine spatial planning and resource management.

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Section: Methodsmentioning

confidence: 99%

Integrating Multibeam Backscatter Angular Response, Mosaic and Bathymetry Data for Benthic Habitat Mapping

et al. 2014

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“…2 have also been used in underwater archaeology to collect bathymetric and backscatter data that were used, for instance, in initial investigations of wreck site locations and extent before divers or ROVs further investigate the sites (e.g. Jones et al, 2005;Masetti and Calder, 2012). Similarly to terrestrial archaeology, these types of data enable both the direct identification of the features on the seafloor or anomalies that may indicate potentially buried artefacts (Papatheodorou et al, 2005).…”

Section: Underwater Archaeologymentioning

confidence: 99%

A review of marine geomorphometry, the quantitative study of the seafloor

Lecours

Dolan

Micallef

et al. 2016

Hydrol. Earth Syst. Sci.

178

120

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Abstract. Geomorphometry, the science of quantitative terrain characterization, has traditionally focused on the investigation of terrestrial landscapes. However, the dramatic increase in the availability of digital bathymetric data and the increasing ease by which geomorphometry can be investigated using geographic information systems (GISs) and spatial analysis software has prompted interest in employing geomorphometric techniques to investigate the marine environment. Over the last decade or so, a multitude of geomorphometric techniques (e.g. terrain attributes, feature extraction, automated classification) have been applied to characterize seabed terrain from the coastal zone to the deep sea. Geomorphometric techniques are, however, not as varied, nor as extensively applied, in marine as they are in terrestrial environments. This is at least partly due to difficulties associated with capturing, classifying, and validating terrain characteristics underwater. There is, nevertheless, much common ground between terrestrial and marine geomorphometry applications and it is important that, in developing marine geomorphometry, we learn from experiences in terrestrial studies. However, not all terrestrial solutions can be adopted by marine geomorphometric studies since the dynamic, fourdimensional (4-D) nature of the marine environment causes its own issues throughout the geomorphometry workflow. For instance, issues with underwater positioning, variations in sound velocity in the water column affecting acousticbased mapping, and our inability to directly observe and measure depth and morphological features on the seafloor are all issues specific to the application of geomorphometry in the marine environment. Such issues fuel the need for a dedicated scientific effort in marine geomorphometry.This review aims to highlight the relatively recent growth of marine geomorphometry as a distinct discipline, and offers the first comprehensive overview of marine geomorphometry to date. We address all the five main steps of geomorphometry, from data collection to the application of terrain attributes and features. We focus on how these steps are relevant to marine geomorphometry and also highlight differences and similarities from terrestrial geomorphometry. We conclude with recommendations and reflections on the future of marine geomorphometry. To ensure that geomorphometry is used and developed to its full potential, there is a need to increase awareness of (1) marine geomorphometry amongst scientists already engaged in terrestrial geomorphometry, and of (2) geomorphometry as a science amongst marine scientists with a wide range of backgrounds and experiences.

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“…The predictive value of these variables is better than that of MBES data (i.e., bathymetric map and backscatter mosaic) as they provide the detailed information on seabed topography and substrata [51,52]. Among the various predictors, the majority of previous research typically used slope, curvature, eastness, and northness derived from bathymetric map and Gray Level Co-occurrence Matrix (GLCM) texture features [53][54][55][56][57] and Angular Range Analysis (ARA) parameters derived from backscatter mosaic [58][59][60]. Though bathymetric map has higher importance in modelling seagrass habitats [12,[61][62][63][64], other backscatter predictors may also contribute significantly to improve the model [62,65].…”

Section: Introductionmentioning

confidence: 99%

Seagrass Habitat Suitability Models using Multibeam Echosounder Data and Multiple Machine Learning Techniques

Muhamad

Hasan

2022

IOP Conf. Ser.: Earth Environ. Sci.

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Seagrass beds are important habitats in the marine environment by providing food and shelter to dugongs and sea turtles. Protection and conservation plans require detail spatial distribution of these habitats such as habitat suitability maps. In this study, machine learning techniques were tested by using Multibeam Echo Sounder System (MBES) and ground truth datasets to produce seagrass habitat suitability models at Redang Marine Park. Five bathymetric predictors and seven backscatter predictors from MBES data were used to representing topography features and sediment types in the study area. Three machine learning algorithms; Maximum Entropy (MaxEnt), Random Forests (RF), and Support Vector Machine (SVM) were tested. The results revealed that MaxEnt and RF models achieved the highest accuracy (93% and 91%, respectively) with SVM produced the lowest (67%). Depth was identified as the most significant predictor for all three models. The contributions of backscatter predictors were more central for SVM model. High accuracy models showed that suitable habitat for seagrass is distributed around shallow water areas (<20 m) and between fringing reef habitats. The findings highlight that acoustic data and machine learning are capable to predict how seagrass beds are spatially distributed which provide important information for managing marine resources.

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Remote identification of a shipwreck site from MBES backscatter

Cited by 15 publications

References 43 publications

Integrating Multibeam Backscatter Angular Response, Mosaic and Bathymetry Data for Benthic Habitat Mapping

Integrating Multibeam Backscatter Angular Response, Mosaic and Bathymetry Data for Benthic Habitat Mapping

A review of marine geomorphometry, the quantitative study of the seafloor

Seagrass Habitat Suitability Models using Multibeam Echosounder Data and Multiple Machine Learning Techniques

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