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Mapping and Classification of Darwin Harbour SeabedGEOSCIENCE AUSTRALIARECORD 2015/18

Justy Siwabessy, Maggie Tran, Zhi Huang, Scott Nichol and Ian Atkinson

In collaboration with Department of Land Resource Management, Northern Territory Government

Department of Industry and ScienceMinister for Industry and Science: The Hon Ian Macfarlane MPParliamentary Secretary: The Hon Karen Andrews MPSecretary: Ms Glenys Beauchamp PSM

Geoscience AustraliaChief Executive Officer: Dr Chris PigramThis paper is published with the permission of the CEO, Geoscience Australia

© Commonwealth of Australia (Geoscience Australia) 2015

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ISSN 2201-702X (PDF)

ISBN 978-1-925124-77-4 (PDF)

GeoCat 79212

Bibliographic reference: Siwabessy, P.J.W., Tran, M., Huang, Z., Nichol, S. & Atkinson, I. 2015. Mapping and Classification of Darwin Harbour Seabed. Record 2015/18. Geoscience Australia, Canberra. http://dx.doi.org/10.11636/Record.2015.018

http://dx.doi.org/10.11636/Record.2015.018

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0BContents

Executive Summary............................................................................................................................... iv

Acknowledgements................................................................................................................................ vi

1 Introduction.......................................................................................................................................... 11.1 Background.................................................................................................................................... 11.2 Objectives...................................................................................................................................... 1

2 Methods............................................................................................................................................... 22.1 Geophysical Acquisition and Processing........................................................................................2

2.1.1 Multibeam Bathymetry and Backscatter Acquisition.................................................................22.1.2 Multibeam Bathymetry Processing...........................................................................................22.1.3 Multibeam Backscatter Processing...........................................................................................4

2.2 Physical Sampling.......................................................................................................................... 42.2.1 Sediment Grab Samples...........................................................................................................52.2.2 Underwater Video Characterisations........................................................................................5

2.3 Seabed Classifications................................................................................................................... 62.3.1 Probability of Seabed Hardness...............................................................................................62.3.2 Seascape Classification............................................................................................................6

3 Results and Discussion....................................................................................................................... 83.1 Geophysical Data........................................................................................................................... 8

3.1.1 Multibeam Bathymetry..............................................................................................................83.1.2 Multibeam Backscatter...........................................................................................................13

3.2 Physical Sampling........................................................................................................................163.2.1 Sediment Grab Samples.........................................................................................................163.2.2 Underwater Video Characterisations......................................................................................21

3.3 Seabed Classifications................................................................................................................. 293.3.1 Seabed Hardness Probability.................................................................................................293.3.2 Seascape Classification..........................................................................................................35

4 Summary and Conclusion.................................................................................................................. 41

References........................................................................................................................................... 42

Appendix A List of Grabs and Footages...............................................................................................44

Appendix B Summary Statistics of Sediment Samples.........................................................................47

Appendix C Summary of Video-derived Substratum and Key Biological Composition.........................50

Mapping and Classification of Darwin Harbour Seabed iii

Executive Summary

This report presents the results of seabed mapping and habitat classification surveys completed in Darwin Harbour during 2011 and 2013 as part of the Northern Territory Government’s marine habitat mapping program. This research aims to provide baseline data on the existing marine habitats and characteristics of the Darwin Harbour region. It is a collaboration between Geoscience Australia (GA), the Australian Institute of Marine Science (AIMS), the Department of Land Resource Management (DLRM) and the Darwin Port Corporation. Key objectives are to:

Produce detailed maps of the bathymetry and derived parameters such as slope and rugosity,

Classify the seabed into areas of hard and soft substrate, and,

Produce seabed habitat maps (or seascapes).

Data collection was completed in two stages comprising a multibeam survey, undertaken on the MV Matthew Flinders in 2011 by DLRMs predecessor, the Department of Natural Resources, Environment, the Arts and Sport (NRETAS), GA, AIMS and the Darwin Port Corporation; and, a seabed sampling survey undertaken in 2013 on the MV John Hickman, by DLRM and GA. Data acquired from the surveys included continuous high-resolution multibeam sonar bathymetry and acoustic backscatter, video and still camera observations of seabed habitats and biological communities, and physical samples of seabed sediments.

Key outcomes from the surveys include:

1. Improved understanding of the seabed of Darwin Harbour. The main seabed geomorphic features identified in Darwin Harbour include banks, ridges, plains and scarps, and a deep central channel that divides into smaller and shallower channels. Acoustically hard substrates are found mostly on banks and are associated with rocky reef and sponge gardens, and are often overlain by a thin veneer of sandy sediment. In contrast, plains and channels are characterised by acoustically soft substrates and are associated with fine sediments (mud and sand).

2. Classification of physical seabed properties to produce a Seascape Map for Darwin Harbour. Six seascape classes (potential habitats) were derived using an Iterative Self Organising (ISO) unsupervised classification scheme. These six classes are related to statistically unique combinations of seabed substrate, relief, bedform and presence of sediment veneer (quite often inferred from presence of epibenthic biota).

The results presented in this report demonstrate the utility of multibeam acoustic data to broadly and objectively characterise the seabed to describe the spatial distribution of key benthic habitats. This is particularly important technique in high-turbidity settings such as Darwin Harbour where the application of satellite and aerial remote sensing techniques can be limited.

The results of this study will be used for the planning and analysis of data from upcoming benthic biodiversity studies as they:

Provide robust near-continuous physical variables that can be used to predictive modelling of biodiversity;

iv Mapping and Classification of Darwin Harbour Seabed

Provide high-resolution coverage of near-continuous variables that describe the key physical characteristic of the seabed of the harbour, and;

Enhance survey sample design by providing indicative locations of likely similar biology communities.

Mapping and Classification of Darwin Harbour Seabed v

Acknowledgements

This study was undertaken by Geoscience Australia, (GA) in collaboration with the Department of Land Resource Management (DLRM) of the Northern Territory Government (NTG). Data used for the study were acquired in 2011 and 2013 on collaborative surveys undertaken by GA, the Australian Institute of Marine Science (AIMS), the Department of Land Resource Management’s (DLRM) predecessor, the Department of Natural Resources, Environment, the Arts and Sport (NRETAS), and the Darwin Port Corporation on board MV Matthew Flinders and MV John Hickman. We thank the master and crew of the MV Matthew Flinders and MV John Hickman for their support in conducting successful surveys in 2011 and 2013, respectively. We thank Mark Matthews of iXSurvey Australia Pty Ltd for conducting the multibeam survey in 2011. Sediment samples were analysed by the Palaeontology and Sedimentology Laboratory, GA. We also thank our reviewers, Kim Picard and Andrew Carroll for their helpful comments on an earlier version of this record.

vi Mapping and Classification of Darwin Harbour Seabed

1 Introduction

1.1 BackgroundIn December 2010, the Northern Territory Government announced funding for high priority environmental monitoring and research activities in Darwin Harbour. Under this funding, the Department of Land Resource Management (DLRM) commenced a two year project in 2011/12 to comprehensively map and describe the seabed habitats of Darwin Harbour (the Darwin Harbour Habitat Mapping Program).

A multibeam sonar bathymetric survey of a priority area of Darwin Harbour was completed in 2011 for the Department of Natural Resources, Environment, the Arts and Sport (NRETAS), in collaboration with Geoscience Australia (GA), the Australian Institute of Marine Science (AIMS), and the Darwin Port Corporation. The survey data was processed, analysed and interpreted by GA to produce a series of high-resolution spatial data products describing seabed characteristics to enable spatial analysis of Darwin Harbour.

In June 2013, a DLRM and GA collaborative seabed sampling survey took place in Darwin Harbour, to ground-truth predictive maps produced from the 2011 survey data. The purpose of the seabed maps is to underpin the development of habitat maps based on flora and fauna community characteristics, which is being undertaken by DLRM as part of the Darwin Harbour Habitat Mapping Program.

1.2 Objectives

The main objectives of the study were; 1) to produce detailed maps of the bathymetry and derived parameters such as seabed slope and rugosity, 2) to classify the seabed into areas of hard and soft substrate, and 3) to produce seabed habitat maps (or seascapes).

The key deliverables from this project include:

High-resolution (1 m) bathymetric and backscatter maps of Darwin Harbour and associated interpretive seabed spatial datasets including slope, rugosity and probability of "rocky" seabed (p-rock);

Spatial datasets from the seascape analysis, and;

A report including methods, a description of physical seabed characteristics based on sediment sample and video analysis, multibeam backscatter data, and tabulated sediment analysis results (i.e. laser particle sizing and percentages mud/sand/gravel).

Mapping and Classification of Darwin Harbour Seabed 1

2 Methods

Specific tasks undertaken during this project include (Figure 2.1):

Acquisition of multibeam bathymetry;

Cleaning and analysis of the multibeam bathymetry dataset to produce a high-resolution bathymetric map;

Collection of representative seabed sediment samples and underwater video transects;

Analysis of seabed sediment samples for texture and grain size;

Analysis of seabed sediment and multibeam bathymetry and backscatter to produce a seabed hardness map;

Interpretation of underwater video transects to provide additional seabed information, and;

An analysis of seabed data to produce a seascape classification for Darwin Harbour.

2.1 Geophysical Acquisition and ProcessingThe multibeam survey of Darwin Harbour was undertaken between 24 June and 20 August 2011 by iXSurvey on board the MV Matthew Flinders (iXSurvey, 2011).

2.1.1 Multibeam Bathymetry and Backscatter Acquisition

A Simrad EM3002D 300 kHz multibeam sonar (MBS) system was used to acquire high-resolution multibeam bathymetry and backscatter data across Darwin Harbour. Multibeam data were recorded using Kongsberg’s Seabed Information System (SIS) software (refer to iXSurvey (2011) for more detail). Motion referencing and navigation data were collected with an Applanix Position and Orientation system, and a C-Nav GPS system (with a horizontal accuracy greater than 0.15 m).

2.1.2 Multibeam Bathymetry Processing

The multibeam bathymetry data were processed using CarisTM HIPS/SIPS v7.1 SP1, and included the application of:

Algorithms that corrected for tide and vessel pitch, roll and heave, and;

Software filters and visual inspection of each swath line to remove any remaining artefacts and noisy data (e.g. nadir noise and data outliers).

To minimise tidal bursts, a co-tidal solution in CarisTM was adopted. A high-resolution bathymetry surface (1 m horizontal resolution and a centimetre vertical resolution) relative to the Mean Sea Level (MSL) was created within CarisTM and then exported as a surface grid (bathymetric map) for display and analysis.

2 Mapping and Classification of Darwin Harbour Seabed

Figure 2.1 Flowchart diagram showing all the processing procedures of mapping and classification of Darwin Harbour seabed.


A common way to calculate derivatives from images is to use a "kernel" filter. A kernel filter typically uses a square or rectangular matrix of values to calculate a new value for the centre pixel based on a function of the cells within the kernel. Kernel dimensions are often odd integer values to maintain symmetry.

Two derivatives, seabed slope and rugosity, were derived using ArcGIS v10. The topographic slope grid represents the slope angle (in degree) of an area of seabed. It was calculated from the bathymetry grid using a square window size of 3 by 3 cells. The rugosity grid calculates the "true" surface area in relation to the planar surface area (Jenness, 2004). It represents the roughness of an area of seabed. Higher values indicate more rugose seafloor. It was calculated from the bathymetry grid using a square window size of 3 by 3 cells.

2.1.3 Multibeam Backscatter Processing

Seabed reflectance (backscatter), which is a surrogate for substrate hardness, was processed using the CMST-GA MB Process v10.10.17.0 toolbox software (co-developed by the Centre for Marine Science and Technology (CMST) at Curtin University and GA (described in Gavrilov et al., 2005a, 2005b; Parnum, 2007)) on the National Computational Infrastructure (NCI) supercomputer at the Australian National University. The fully processed backscatter intensities were corrected for transmission loss and insonification area, and normalised to the transmitted pulse length. Processing steps included: removal of the system transmission loss; removal of the system model; calculation of the angle of incidence; correction of the beam pattern; calculation of the angular backscatter response in a sliding window, and; removal of the angular dependence and restoration to the backscatter strength at an angle of 40°.

The toolbox calculates the backscatter coefficient corrected for transmission loss and insonification areas. Calculation of the insonification area is based on the equation given in Talukdar et al. (1995). With these measurements, the corresponding incidence angle, coordinates on the seabed (x,y) and depth (z) are calculated. A sliding window approach was used to remove of the angular dependence, based on a 50% overlap in a 1° bin of incidence angle (Gavrilov et al., 2005a, 2005b; Parnum, 2007).The final processed backscatter data were then gridded to 1 m horizontal resolution and exported for display and analysis (Figure 2.1).

Angular backscatter response

In the process of removing the angular dependence from the backscatter for a consistent backscatter strength across the swath in various incidence angles (for a homogeneous seafloor), the angular backscatter response was calculated in a 1° bin of incidence angle and averaged within the sliding window. The angular backscatter response is an intrinsic property of the seafloor, illustrating that the backscatter strength changes with the angle of incidence and is dependent on substrate type.

2.2 Physical SamplingThe Darwin Harbour sediment survey was completed during 17–22 June 2013 using the MV John Hickman. Seafloor sediment samples were collected using a mini Van-veen sediment grab and a mini Shipek sediment grab. Seabed imagery was obtained simultaneously using a real-time underwater video camera system. The sampling locations were randomly selected with an even number of samples allocated to each backscatter and depth range.


2.2.1 Sediment Grab Samples

2.2.1.1 Seabed Composition and Texture

Sieve Grain Size

Sediment samples were oven dried at 40°C and soaked in a 5% calgon solution to aid the breakdown of sediment aggregates to disperse clay particles of sediments. Sediments were then washed through stainless steel sieves into the following fractions to establish particle size distribution: mud <63 µm; sand 63-2,000 µm; and gravel >2,000 µm. After samples were dried, the material for each fraction was then weighed with an analytical balance to obtain the percentage of mud, sand and gravel in each sample.

Laser Grain Size

Bulk grainsize distributions for the sand and mud fractions were determined using the Malvern Mastersizer 2000 laser particle size analyser. The instrument uses laser diffraction to calculate the volume weighted percentage of particles in the sample. Samples were initially treated with a 10% hydrogen peroxide solution to remove organic matter from the sample. Any particles over 1.7 mm in size were noted and removed from the samples prior to analysis. The particle size produces histograms of grain size ranging from 0.02 µm to 2,000 µm. Additional statistics were also calculated, including mean, median, standard deviation, skewness and kurtosis.

Calcium Carbonate Content

Carbonate concentrations were determined on the bulk, mud and gravel sieve grainsize fractions using the carbonate digestion method. The samples were ground to a fine powder and reacted with 25% orthophosphoric acid in a canister connected to a digital pressure gauge. The pressure of CO2 gas release for a known quantity of sample was converted into percentage of calcium carbonate. This was calculated from formulas derived from regular pure calcium carbonate calibration runs.

2.2.2 Underwater Video Characterisations

2.2.2.1 Substratum and Geomorphology Classifications

Video analysis was conducted at GA, adopting a similar method described in Mortensen and Buhl-Mortensen (2004) and Li et al. (2013). The video footage was classified in 15 second sequences of seabed sections at 30 second intervals along each transect for substratum composition and geomorphology (e.g. bedform and relief). Substratum composition (e.g. rock, rubble, boulder, cobble, pebble, gravel, sand, and mud as defined by the Wentworth scale) was estimated to a precision of 10% (0, 10, 20…100%) (Wentworth, 1992). Geomorphology was defined as local "vertical relief" (e.g. flat [0-0.3 m], low [0.3-1 m], moderate [1-3 m]) and "bedform" (e.g. ripples, waves, bioturbation). Substratum composition was further analysed in conjunction with any sessile biota present. If a majority of the video characterisation appeared to be sediment, but supported biological entities that required hard substratum to grow, then the proportion of sediment covered with biota was inferred as hard substratum (and was classified as veneer). Video footage was recorded where sediment samples were collected.


2.2.2.2 Hardness Classifications

Underwater video observations of sample stations were further used to describe benthic habitats in terms of hardness (i.e. "hard" and "soft" in both primary and secondary substrata). Rock, boulders, cobbles, pebbles and gravel identified in either primary or secondary substrata were merged to form the "hard" class, whereas sand and mud found in both primary and secondary substrata were combined to form the "soft" class. For example, if the primary substratum was classed as rock and the secondary substratum was classed as mud, this would result in a "hard-soft" classification. On this basis, benthic habitats in Darwin Harbour were classified into four broad categories; "hard", "hard-soft", "soft-hard" and "soft".

2.3 Seabed Classifications

2.3.1 Probability of Seabed Hardness

Using the bathymetry grid, underwater video and results of the sediment analysis, stations with flat, sand-covered seabed from multiple samples using mean grain sizes as a factor were identified and used to derive theoretical angular backscatter response curves (N.B. residuals from the measured curves were used to correct for beam pattern). We found insignificant differences from our prior calibration exercise and hence no correction or modification to the derived angular backscatter response curves was necessary.

The angular backscatter response curve data was analysed to produce a dataset indicating the probability of seabed hardness. The underwater video-derived hardness classifications were first used with existing seabed hardness data from DLRM to identify known areas of "rocky" seabed. The angular backscatter response curves were then extracted from the identified "rocky" areas and an average of the angular backscatter response curves was calculated. This was completed to determine a reference curve of "rocky" seabed. This curve was used as a reference and compared to all other angular backscatter response curves using the Kolmogorov-Smirnov goodness of fit test to estimate the probability of the "rocky" seabed (p-rock). Finally, the Inverse Distance Weighted interpolation technique was used to produce a continuous layer of the p-rock for the whole study area.

2.3.2 Seascape Classification

The assumption that physical properties can be used as surrogates to characterise seabed habitat is central to the classification approach adopted here. For example, water depth is an indirect surrogate for the distribution of benthic species as it is correlated with temperature, pressure, light availability and food supply. It therefore exerts a first order control over the occurrence of species in the oceans (Etter and Grassle, 1992; Grassle and Maciolek, 1992).

Slope influences the distribution of the biota on the seabed as it is correlated to the local hydrodynamic regime (i.e. currents), which is associated with food provision (Burrough and McDonnell, 1998). Rugosity often accounts for the variability in the spatial distribution of benthic habitats, which is associated with changes in seabed geomorphology (Brown et al., 2002, Edwards et al., 2003). Slope and rugosity, among other terrain variables, are strongly correlated with the abundance and distribution of coral at certain analysis scales (Dolan et al., 2008; Guinan et al., 2009; Li et al., 2009; Tong, 2012; Siwabessy et al., 2013).


Multibeam backscatter is also an indirect surrogate for determining habitat type. It is known to have a significant relationship with substrate type, which in turn, is related to habitat type (e.g., Greene et al., 1995; Auster and Langdon, 1999; Pitcher et al., 2002; Post et al., 2006). Many factors influence backscatter, including incidence angle, acoustic frequency, roughness scales, grain size distribution, presence of fauna and flora, biological reworking, and volume reverberation (Jackson et al., 1986; de Moustier and Alexandrou, 1991; APL, 1994; Hughes-Clarke, 1994; Lyons et al., 1994; Talukdar et al., 1995; Jackson et al., 1996; Novarini and Caruthers, 1998; Williams et al., 2002; Siwabessy et al., 2006a; Parnum, 2007; Fonseca and Mayer, 2007; De Falco et al., 2010; Gavrilov and Parnum, 2010; Hamilton and Parnum, 2011; Hasan et al., 2012).

Backscatter intensities as a function of angle of incidence can also be used as a surrogate for habitat classification as it is correlated with seabed type (Hamilton and Parnum, 2011; Siwabessy et al., 2013). For example, the backscatter strength resulting from small incidence angles is generally higher than larger incidence angles. This is due to the respective coherent versus incoherent returns arising from signal scattering (de Moustier and Alexandrou, 1991). This property is generally unique to each seabed type. For example, sand and mud show rapid decreases in the backscatter strength toward the outer swath angles, whereas dense seagrass beds produces backscatter strength independent of the incidence angle (Siwabessy et al., 2006b), that is, a relatively flat backscatter response towards the outer swath angles.

Multiple spatial layers of physical data were classified using the Iterative Self Organising (ISO) Unsupervised Classification methodology available in ArcGIS (v.10). This methodology performs unsupervised classification based on a series of input raster bands using the ISO Cluster and Maximum Likelihood Classification tools. Datasets used in the derivation of the seabed habitat classification included bathymetry, slope, rugosity, backscatter and p-rock (Figure 2.1).

Statistically, there are an optimum number of classes into which the data can be partitioned that minimises uncertainty. There are a number of ways to determine the optimal number of classes including distance ratio and Calinkski-Harabasz pseudo F-statistics (Hinde et al., 2007). The method we used is called the Distance Ratio method. Classifications were carried out for 2 to 9 classes, with the distance ratio estimated for each class. The distance ratio is the ratio of the average of the mean distance of each class member from its class mean to the overall average distance of each member from the overall mean. This value provides an indication of how well the data matches the assigned classification. The optimal number of classes occurs where the distance ratio has a local minimum, indicating that the addition of more classes will not improve the classification accuracy as much as the addition of previous classes. Choosing fewer classes will not explain the variation in the classes as thoroughly.


3 Results and Discussion

3.1 Geophysical Data

3.1.1 Multibeam Bathymetry

A total of 180 km2 of multibeam bathymetry data was acquired (Figure 3.2), showing that the seafloor of Darwin Harbour is complex and irregular with water depths varying between 1 and 43 m (relative to MSL) (Figure 3.3). The 15 m depth contour encloses areas along the shallow edges of the harbour whereas the 30 m depth contour surrounds the deepest central channel within the harbour. Primary geomorphic features include channels, banks, ridges, plains and scarps. The area is characterised by a deep mid-harbour channel, which sinks to approximately 35-40 m, and splits into narrower and shallower channels which rise to around 20 m depth. Most areas adjoining the coast are typically shallow at approximately 1 m below the sea surface. Previous spatial data received from DLRM indicates the presence of reefs adjacent to this area. The area within Cullen and Fannie Bays in the central eastern side of the Harbour is characterised by a shallow plain at 3 and 5 m water depth, extending from the near shore out to the entrance where it meets a low-lying linear ridge (Figure 3.3).

Slope values vary from 0 to 18 (Figure 3.4). High slope values are typically associated with the edges of more elevated seabed such as ridges found around Fannie Bay, the entrance of the harbour in the northwest and banks in the eastern area of the West Arm. More rugged seabed occurs in the main, deep, mid-harbour channel and the channel in the Middle Arm of the harbour. Low slope values are associated with low-flat relief seabed found in the majority of the areas within the harbour.

Rugosity values vary between 1 and 1.03 (Figure 3.5). Low rugosity values are found mainly in the shallow areas and are also associated with low slope values. Areas with more rugged seabed occur in the main channel, the ridge near Fannie Bay and in the smaller channel to the southeast in the Middle Arm where the higher slopes are associated with higher rugosity values.


Figure 3.2 Location map showing the multibeam coverage and the sample station numbers. Data supplied from DLRM is indicated in hashed polygons on figure (e.g. Intertidal and subtidal outlines).


Figure 3.3 False colour bathymetry image with main geomorphic features overlayed with intertidal and subtidal hard substrate, and 15 m and 30 m depth contours. The bathymetry data is available from the following link http://www.ga.gov.au/metadata-gateway/metadata/record/gcat_74915.


http://www.ga.gov.au/metadata-gateway/metadata/record/gcat_74915

Figure 3.4 False colour seabed slope map of Darwin Harbour seabed. The derived slope data is available from the following link http://www.ga.gov.au/metadata-gateway/metadata/record/gcat_75390.



Figure 3.5 False colour seabed rugosity map of Darwin Harbour seabed. The derived rugosity data is available from the following link http://www.ga.gov.au/metadata-gateway/metadata/record/gcat_75393.



3.1.2 Multibeam Backscatter

In general, backscatter values (Figure 3.6) correlates well with geomorphic features (Figure 3.7). High backscatter (darker colour in Figure 3.6) is typically associated with shallow reefs, banks (the highest), ridges and more elevated features, while lower backscatter (lighter colour in Figure 3.6) is associated with soft sediments found mostly within channels or depression features and plains (the lowest) (cf Figure 3.6 and Figure 3.7). The sandy plain area within Cullen and Fannie Bays (mean backscatter strength ~-30 dB) is surrounded by areas associated with high backscatter strength; shallow reef sections and a large bank at the central western end of the Harbour (mean backscatter strength ~-10 dB) (Figure 3.6). On average, the difference between these two extremes of the mean backscatter strength is ~ 18–20 dB. This dynamic range is large enough to allow for multiple substrate types.


Figure 3.6 False colour backscatter image with main geomorphic features relating to backscatter strength variations. The backscatter data is available from the following link http://www.ga.gov.au/metadata-gateway/metadata/record/gcat_74916.



Figure 3.7 Boxplot of backscatter strength in relation to main geomorphic features highlighted in Figure 3.6.


3.2 Physical Sampling

3.2.1 Sediment Grab Samples

During the 2013 survey, 61 sediment samples were successfully collected at 59 locations. At three locations sampling was unsuccessful so these locations were re-sampled. However, no sediment was recovered at these locations due to the presence of hard seabed features (Figure 3.2 and Appendix Table A.1). A total of 88 sediment samples (61 from the 2013 survey and additional 27 received separately from DLRM) (Appendix Table B.2) were used in this study.

Visual observations of the grab samples retrieved on the boat indicate that the seabed sediments of Darwin Harbour vary and included mud, fine- to medium-grained muddy sand, shell grit, medium- to coarse-grained gravel, pebble and cobble, and rocky reef. Intertidal areas adjacent to the coast are mainly characterised by mud and muddy fine- to medium-grained sand (Figure 3.8). Sediments in channels are mainly comprised of shell grit and coarse-grained sand, with large cobbles also found in the channel adjacent to Cullen Bay. Biological habitats such as sponge, octocoral and hard coral patches are also identified from the underwater video.

The grainsize analysis also indicates that the mean of %mud, %sand and %gravel was 21%, 53% and 26%, respectively. Of the 88 sediment samples, 47 had %sand greater than 50% and 14 had %gravel greater than 50% whereas only 8 samples had %mud greater than 50% (Appendix Table B.2). This suggests that sandy sediments dominate the seabed in Darwin Harbour (Figure 3.8 and Figure 3.9). Analysis of the fine sediments (<2,000 µm) show that the mean grain size varied from 16 m (mud) to 1053 m (coarse sand) and a mean of 365 m (medium sand) (Figure 3.10 and Appendix Table B.2).

An increasing trend of backscatter strength with mean grain size was shown from this study, supporting results from previous studies (Figure 3.11) (Jackson et al., 1986; de Moustier and Alexandrou, 1991; APL, 1994). Of the 88 samples (Appendix Table B.2), mud was found at 5 locations, which correlates with areas of low to medium backscatter strength. For example, CaCO3 sand fraction comprised less than 25% of sediment samples collected from the main channel and the East Arm (Figure 3.10) which was characterised by low to medium backscatter strength. Fine sand, recorded at 29 locations (Appendix Table B.2) was found mostly within soft, sandy plain features and was associated with low backscatter strength and with sand fraction CaCO3 > 50% (Figure 3.10). Medium coarse sand, found in 44 locations, are (Appendix Table B.2) mostly associated with areas of high backscatter strength such as banks, ridges and more rugged, elevated features (Figure 3.10). They are also generally associated with sand fraction CaCO3 between 25% and 50% (Figure 3.10).


Figure 3.8 Map showing the spatial distribution of the sediment grain size in relation to backscatter distribution.


Figure 3.9 Folk classification diagram showing the grain size distribution.


Figure 3.10 Map showing the spatial distribution of the Wentworth grain size category and the sediment fraction in relation to backscatter distribution.


Figure 3.11 Boxplot of backscatter strength grouped into Wentworth grainsize categories shown in Figure 3.10.


3.2.2 Underwater Video Characterisations

3.2.2.1 Substratum and Geomorphology

Underwater video was acquired at 35 stations within Darwin Harbour during 2013, in water depths varying between 2 and 37 m (Figure 3.2). Underwater video was acquired from an additional 33 stations by DLRM in water depths ranging between 4 and 33 m and analysed using the same classification system (Figure 3.12). Appendix Table C.3 shows only results from data acquired by GA in 2013.

Video observations correlated highly with backscatter strength, where soft seabed substrates were recorded in areas of low backscatter strength such as plains. Hard seabed substrates occupied areas of high backscatter strength, such as banks, ridges and more rugged and elevated seabed features (cf Figure 3.3 and Figure 3.12). However, there were some discrepancies between the video observations and backscatter strength at some locations. For example, three stations located in the main channel were comprised of hard-dominated seabed and associated with low backscatter strength and high mud composition and were therefore referred to as "mud veneer". Conversely, soft-dominated substrates found on the plain in Fannie Bay and the corner between East and Middle Arms and between Middle and West Arms were associated with high backscatter strength and high sand composition and were therefore referred to as "sand veneer" in three other locations (Figure 3.13). One possible explanation for this discrepancy may lie within comparing the scale of view between the underwater video (point source) relative to the continuously-acquired backscatter data, and reconciling this difference between the two data types.


Figure 3.12 Map showing the spatial distribution of the video substratum as pie charts in relation to the backscatter distribution.


Figure 3.13 Map showing the spatial distribution of video seabed hardness in relation to the backscatter distribution.


3.2.2.2 Seabed Environments

Analysis of the underwater video shows that the video category "soft" (Figure 3.14a) was associated with very low backscatter strength and was mainly found in shallow water depths (6-8 m) where the substrate was flat and dominated by mud (70%) (Figure 3.15 and Table 3.1). Bioturbated and barren habitats dominate the "soft" category.

The "soft-hard" video category (Figure 3.14b) associated with low acoustic hardness was found in very deep waters (Table 3.1). The substrate was dominated by mud (42%), sand (32%) and gravel (20%). The dominant habitats in Darwin Harbour were comprised of bioturbated sediments and mixed patches of sponges and octocorals in flat to low relief. The presence of mixed patches of epibenthic biota slightly increased the acoustic hardness response, potentially due to the production of calcium carbonate or aragonite from hard corals to form reefs, or the colonisation of epibenthic biota on hard substratum. These factors can increase the potential of an increased backscatter response.

Table 3.1 Summary of the four broad video categories for the Darwin Harbour.

Video category Depth Acoustic

hardness Relief Substrate Habitats*

Soft Shallow(8 m)

Very Low(-27.62 dB)

Flat Sandy Mud BioturbatedBarren

Soft-Hard Very Deep(32 m)

Low(-23.24 dB)

Flat-Low Gravelly Sandy Mud BioturbatedMixed Patches

Hard-Soft Deep(24 m)

High(-22.46 dB)

Low-Flat Gravelly Sandy Rocky Mud BioturbatedMixed PatchesMixed Gardens

Hard Very Shallow(6 m)

Very High(-17.52 dB)

Low-Flat Sandy Muddy Rock Mixed PatchesMixed GardensMixed Gardens (HC)

*Please refer to Carroll et al. (2012) and Przeslawski et al. (2011) for descriptions of habitat types.

The video category "hard-soft" (Figure 3.14c) was generally found in the deep water and was characterised by high acoustic hardness (Table 3.1). Seafloor sediments consisted of gravelly, sandy, rocky mud where mud and rock accounted for 25% and 21% of the total substrate composition, respectively. The dominant habitats were bioturbated sediments, mixed patches of sponges and octocorals and mixed gardens of sponges and octocorals with flat or low relief. The marginal proportion of rock and the presence of mixed gardens resulted in a moderately high acoustic hardness.

The video category "hard" (Figure 3.14d) was characterised by very high acoustic hardness and was found mostly in the very shallow waters of the harbour where relief low to flat. This category contained a significant proportion of rock (40%) compared to other video categories. The dominant habitats were mixed patches of octocorals and sponges, mixed gardens and mixed gardens with hard corals, which reflected the availability of hard substrata suitable for the colonisation of sessile filter-feeders and reef-building corals, and, consequently, the formation and establishment of larger areas of hard substratum. The presence of mixed gardens with hard corals made this video category unique from the "hard-soft" video category and, together with the significant proportion of rock, was responsible for increasing acoustic hardness. Mixed gardens and mixed gardens with hard corals consisted mostly of habitat-forming biota, namely, sponges, octocorals, hard corals and macroalgae (Figure 3.14d). Most of the areas of reef, banks, rocks and ridges found in the DLRM data were also associated with the "hard"


habitat category defined by the underwater video (Figure 3.5). In general, seabed hardness, ground-truthed using video analysis, shows a positive relationship with backscatter strength (Figure 3.15).

Figure 3.14 Representative underwater video images from the Darwin Harbour survey 2013. a) "soft" class with shell debris and medium-grained sand sediments, b) "soft-hard" class with muddy-sand sediments and sparse cover of epibenthic biota (sponges), c) "hard-soft" class with thin veneer of sediment over hard substratum indicated by hydroids, gorgonians and sponge growth, and d) "hard" class with large sponge and octocoral growth, and evidence of exposed bedrock.


Figure 3.15 Boxplot of backscatter strength grouped into video substratum categories.


There were some discrepencies between identified veneer types from the underwater video and the hardness classification, where a veneer attribute was noted due to the presence of epibenthic biota, see Section 3.2.2.1 (Figure 3.13). Samples identified as "sand veneer" were re-classified as "hard" due to high backscatter intensity, whereas samples identified as "mud veneer" were not re-classified and remain misclassified (e.g. samples in the main channel). However, samples classified as ‘soft’ with no indication of veneer from the underwater video (the majority of samples were labelled as "sand veneer") remain a problem and may potentially be related to the issue of reconciling scale resolution identified in Section 3.2.2. Samples associated with high %mud and/or %sand were also associated with soft seabed hardness (as observed in underwater video) and low backscatter strength (Figure 3.8 and Figure 3.9). As the percentage amount of gravel increased, the backscatter and the seabed hardness also increased (Figure 3.8 and Figure 3.9).

From the video, sediment and geomorphological analyses, the lowest backscatter strength was associated with soft and shallow areas, and sand plains. These environments typically show an angular backscatter response peaking within a narrow band and dropping substantially towards the outer swath angles. This correlates with observations of the angular backscatter response curve of the "soft" seabed video classification (Figure 3.16).Bioturbation noted from the video also appeared to increase relative to the availability of soft-sediments. The highest backscatter strength was associated with very shallow areas of hard, consolidated and rugose seafloor such as shallow reefs, banks, ridges and elevated features. These environments typically show an angular backscatter response with a gentle slope towards the outer swath angles.


Figure 3.16 Angular response curves associated with the four broad video categories.


3.3 Seabed ClassificationsPhysical seabed datasets are an important addition to a marine manager’s toolkit. The potential mobility and stability of the seabed sediments, which can be assessed using the bathymetry, hardness, slope and rugosity datasets, play an important role in guiding the location and extent of infrastructure developments. For fisheries management, a combination of the bathymetry, rugosity and the hardness layers can provide a surrogate for reef localities and therefore potential reef fish distribution.

Hardness and seascapes classifications of these physical datasets provide an objective guide for the identification of distinct habitats, and hence distinct benthic communities. However, for each new area surveyed it is not possible to know, based only on the physical properties used to predict these classifications, if the benthic communities also change at the same locations. For example, there may be such a small change in water depth the communities are not actually impacted. There may also be other confounding influences, such as seawater temperature or suspended sediment that could also play a role in controlling the spatial distribution of a benthic community.

For this reason, the seascape classification presents an initial stratification of the physical environments, which generally serves as a good indicator of where scientists and managers may choose to focus their efforts. However, it is recommended that further ground-truthing be undertaken to assess if the changes in physical parameters really do illicit a corresponding change in biological communities. Furthermore, seabed datasets assist in identifying and managing environmental values, and form the basis for monitoring programs.

3.3.1 Seabed Hardness Probability

The locations of areas of rocky reef were identified using the information provided from the bathymetry and backscatter grids, the additional underwater video transects and the hard ground shapefile received from the DLRM (Figure 3.2). The average angular response curve for the reef/rocky seabed was calculated and compared to all other derived angular response curves to determine the actual probability of "rocky" seabed (p-rock). The results of the probability of rocky seabed (p-rock) analysis show that areas of high p-rock are patchy and localised to narrow areas between and along channels (Figure 3.17). The predicted "rocky" areas correspond with data received from the DLRM. The p-rock map also shows a pipeline to the north of Wagait Beach, but fails to show any other known pipelines in the harbour (which could indicate that the pipelines may be masked by large amounts of soft sediment and possibly linked with the highly dynamic hydrodynamic system present in Darwin Harbour).

Cross-sections of the harbour, both in the central region and towards the mouth, confirm that high p-rock values are generally associated with banks, ridges and other elevated geomorphic features as well as the sediment veneer identified within the shallow sandy plain (Figure 3.18 and Figure 3.19). These features are also associated with high backscatter signals. The profiles also highlight the positive association between p-rock and high backscatter intensity. There are a few locations where the video, backscatter and p-rock data do not correspond, especially in areas where a soft sediment veneer has been identified from the video. In almost all areas of misclassification, the identified substratum composition from the seascape classification suggests a "soft" classification, whereas the presence of sessile biota suggests the potential existence of sandy or muddy veneer on top of hard substratum. It is the hard, shallow sub-bottom underneath a few decimetres of veneer that results in a high backscatter signal. This becomes a problem that cannot be resolved where discrepancies between the video, the


backscatter and p-rock data cannot distinguish the presence of veneer as there is no indication of sessile biota. The results are consistent with the current state of knowledge on geoacoustics, namely, that most acoustically hard substrates are dominated by hard sediments, whereas most acoustically soft substrates are associated with soft, finer-grained sediments (Figure 3.8). While it is expected that high p-rock correlates with high backscatter and a "hard" seabed video category, there are few locations were they do not. These include areas where a "soft" sediment veneer has been identified in the video data, but is classified with a high p-rock value as indicated above.

The results are supported by previous studies which have shown that backscatter strength increases with the mean grain size (APL, 1994; Goff et al. 2000; Briggs et al., 2001, 2002; Ferrini and Flood 2006; Sutherland et al., 2007; De Falco et al. 2010) (cf Figure 3.8 and Figure 3.9). Finer-grained (muddy) sediments generally produce low backscatter intensity whereas coarser (gravel, cobble, boulder and rocky) sediments are more likely to produce higher backscatter intensity. This effect is due to higher density and sound speed properties in hard substrates, larger scale of apparent roughness, and porosity (APL, 1994; Goff et al. 2000; Briggs et al., 2001, 2002; Ferrini and Flood 2006; Sutherland et al., 2007; De Falco et al. 2010). Additionally, comparison between p-rock and video categorization of the seabed hardness shows that p-rock >0 are associated with "soft-hard", "hard-soft" and "hard" categories (Figure 3.20). Stations associated with the "hard-soft" and "soft-hard" categories have quite low rock probability (p-rock 0.1) while those associated with the "hard" category have p-rock 0.4. Note that the range and standard deviation of p-rock of the hard class is very high, which suggests that the derived p-rock layer includes other potential hardness categories, such as "sand" and "mud" veneer.


Figure 3.17 False colour p-rock map of the Darwin Harbour seabed. The derived p-rock data is available from the following link http://www.ga.gov.au/metadata-gateway/metadata/record/gcat_83950.



Figure 3.18 (a) Water depth, (b) backscatter intensity, and (c) p-rock profile for a cross-section of Darwin Harbour (see Figure 3.2 for location) showing high backscatter values associated with a bank feature and lower values in channels.


Figure 3.19 (a) Water depth, (b) backscatter intensity, and (c) p-rock profile for a cross-section of outer Darwin Harbour (see Figure 3.2 for location) showing high backscatter values associated with a ridge feature and thin sand veneer, and low backscatter values associated with a soft sandy plain.


Figure 3.20 Boxplot of p-rock grouped into video category at sampling stations.


3.3.2 Seascape Classification

The results of the seascape classification, using the physical parameters presented in previous sections, and the distance-ratio analyses, suggest an optimal number of six seascape classes (Figure 3.21). Characterisation and description of each seascape is based on the statistics for each parameter (Figure 3.22). This suggests that the result for more than 6 classes does not significantly improve the classification accuracy further whereas the result for less than 6 classes will not optimally describe the variation in the classes.

Seascapes are divided strongly by water depth, where an increase in seascape class number corresponds to a decrease in water depth. Seascape 1 is confined to the deepest areas of the harbour, i.e. in the main channel, while seascapes 5 and 6 are confined to the shallowest depths of the harbour (Figure 3.23).

The parameters differentiating seascape 1 and 2 from the other seascapes, but also between each other, are slope, rugosity and backscatter response (Figure 3.9). While seascape 1 is characterised by steep, rugose, rocky and muddy seabed, seascape 2 is characterised by flat, smooth and gravelly sandy-muddy seabed. This correlates with the low relief observed in the video for seascape 1 and flat relief for seascape 2 (Table 3.1). The steep negative backscatter gradient observed in the inner angles of incidence in seascape 2 supports evidence of smoother substrate (Figure 3.24).

Consequently, the gentle backscatter gradient observed for seascape 1 supports a more rugose substrate (Figure 3.24). The dominant habitats observed for seascape 1 and 2 supports the previous arguments, with respective mixed patches and mixed gardens with bioturbated substrates, barren and mixed patch substrates.

Seascape 3 is an intermediate class, mostly characterised by mid-range values in most parameters (Table 3.1). The dominant habitats are similar to seascape 2, i.e. bioturbated, barren and mixed patches (Table 3.1), with flat relief and gravelly rocky sandy mud substrate. The marginal presence of rock is responsible for an increase in the acoustic hardness differentiating this seascape from seascape 2.

Seascape 4 is differentiated from the others due to the high p-rock values and is mostly located between seascapes 6 and 3 in Darwin Harbour (Figure 3.23). This seascape is characterized by gravelly rocky sandy mud and an increased seabed roughness (Table 3.1). Hence, it may be seen as a transition between seascapes 6 and 3. The dominant habitats are mixed patches, bioturbated, barren and mixed gardens, with flat or low relief (Table 3.1). The presence of mixed gardens and rocks increases the acoustic hardness and roughness of the substrate found in this seascape.

Seascape 5 and 6 occupy the shallowest water depths and extreme opposite values of backscatter and p-rock values. Seascape 5 is characterised by the lowest values of backscatter and p-rock, and represented by flat, sandy mud substrate on sediment plains, while seascape 6 is characterised by the highest backscatter and p-rock values and corresponds to very shallow, gravelly, sandy, rocky consolidated substrate (cf Figure 3.17 and Figure 3.23). The dominant habitats corresponded with bioturbated and barren habitats for seascape 5, and bioturbated and mixed gardens for seascape 6.


Table 3.2 Summary of the six seascape.

Seascape (% of area)

Video Category Depth (m) Acoustic

hardness Relief Substrate Habitats

1 (7%) Hard-Soft Very deep (32 m)

Low (-24.65 dB)

Low Rocky mud Mixed PatchesMixed GardensBioturbated

2 (23%) Soft-Hard Deep (24 m)

Low (-24.65 dB)

Flat Gravelly sandy mud BioturbatedBarrenMixed Patches

3 (24%) Hard-Soft Moderately deep (18 m)

Moderately High (-22.40 dB)

Flat Gravelly rocky sandy mud

BioturbatedBarrenMixed Patches

4 (15%) Hard Moderately shallow (13 m)

High (-19.77 dB)

Flat-Low Gravelly rocky sandy mud

Mixed PatchesBioturbatedBarrenMixed Gardens

5 (16%) Soft Shallow (8 m)

Very low (-26.98 dB)

Flat Sandy mud BioturbatedBarren

6 (14%) Hard Very shallow (6 m)

Very High (-17.80 dB)

Low-Flat Gravelly sandy rocky mud

BioturbatedMixed PatchesMixed GardensMixed Gardens (HC)


Figure 3.21 Plot showing the distance ratio for 9 classes of seascapes. A total of six seascapes were defined for the Darwin Harbour seabed based on the first local minimum.


Figure 3.22 Graphs of seascape versus: (a) water depth; (b) backscatter; (c) slope; (d) rugosity; (e) p-rock. Three highest and lowest means used as distinguishing properties for naming each of the seascapes. Plots showing means and error bars of one standard deviation ranked in order of increasing values.


Figure 3.23 Darwin Harbour survey area, showing the six seascape classes. The seascape classification layer is available from the following link http://www.ga.gov.au/metadata-gateway/metadata/record/gcat_83951.



Figure 3.24 Mean angular response curves of the seascape classes shown in Figure 3.21.


4 Summary and Conclusion

The seabed of Darwin Harbour is dominated by a major channel which branches upstream into three smaller channels to the south/southeast. A number of banks, rocky reefs and plains are observed. The results from this study are consistent with the current state of knowledge on geoacoustics and highlight the strong relationship between backscatter strength and mean grain size, whereby acoustically hard seabed features are dominated by hard substrates and relatively coarse seabed sediments, and acoustically soft substrates are dominated with soft and muddy sediments.

To verify and validate the acoustic classification, underwater video and sediment samples were used. These ground-truthing techniques were successful in delineating clear differences between habitat classes and highlighting areas where sessile biota are present on a veneer of soft sediment atop hard substratum. Thin sediment veneers over hard ground allow the penetration of acoustic energy below the surface and subsequently record strong backscatter signals. Unfortunately, these approaches provide little (if any) information about the sub-surface greater than a few decimetres, and consequently, may misclassify areas of hard ground as soft grounds. Other remote-sensing or sampling devices such as seabed penetrometers or acoustic devices such as high-frequency sub-bottom profilers could be used to confirm the presence of hard substrate in these settings.

The ISO Unsupervised Classification methodology provides a first-pass classification of multiple spatial layers of physical data from multibeam sonar. This classification suggests that a statistically optimal number of 6 seascapes are adequate to characterise the seabed environments of Darwin Harbour. The results from this study provide:

high-resolution coverage of near-continuous variables that describe the key physical characteristic of the seabed of the harbour;

robust near-continuous physical variables that can be used to predictively model biodiversity;

indicative locations of likely biology communities to enhance future sample design;

strong foundations for the planning and analyses of upcoming surveys targeting benthic characterisation and biodiversity.


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Appendix A List of Grabs and Footages

Appendix Table A.1 List of grabs and footages.

Sequence Survey ID Station Sample ID Longitude Latitude Depth Operations Remarks

1 GA0341 01 GA0341/01GR01 130 49.1887 -12 26.8737 5 Mini Van-veen Fine sand silt

2 GA0341 02 GA0341/02GR02 130 48.9198 -12 26.7766 4 Mini Van-veen Fine sand medium coarse shell and coral grit

3 GA0341 03 GA0341/03GR03 130 49.1378 -12 25.7667 6 Mini Van-veen Fine sand slit

4 GA0341 04 GA0341/04GR04 130 49.3637 -12 26.0673 5 Mini Van-veen Very fine sand silt

5 GA0341 05 GA0341/05GR05 130 49.7797 -12 27.9007 8 Mini Van-veen Silt fine sand

6 GA0341 06 GA0341/06GR06 130 51.5812 -12 28.2710 4 Mini Van-veen Silt fine sand fine shell grit

7 GA0341 07 GA0341/07GR07 130 55.9082 -12 29.9916 7 Mini Van-veen Coarse sediment fine gravel with silt

8 GA0341 08 GA0341/08GR08 130 56.2108 -12 30.7724 10 Mini Van-veen Silt fine to coarse unsorted sand

9 GA0341 09 GA0341/09GR09 130 55.5151 -12 30.4651 5 Mini Van-veen Gravel very coarse unsorted

10 GA0341 10 GA0341/10GR10 130 55.482 -12 30.085 8 Mini Van-veen Coarse shell grit sand

11 GA0341 11 GA0341/11GR11 130 54.3998 -12 30.6244 18 Mini Van-veen Silt fine sand medium shell grit

12 GA0341 12 GA0341/12GR12 130 53.1540 -12 30.4921 11 Mini Van-veen Mud silt shell grit

13 GA0341 13 GA0341/13GR13 130 52.4066 -12 29.8032 15 Mini Van-veen Unsorted coarse gravel and sand

14 GA0341 14 GA0341/14GR14 130 51.9063 -12 29.6834 10 Mini Van-veen Silt fine shell grit

15 GA0341 15 GA0341/15GR15 130 52.3867 -12 28.6540 6 Mini Van-veen Mud very fine silt

16 GA0341 16 GA0341/16GR16 130 51.1502 -12 27.6802 5 Mini Van-veen Unsorted shell fragment silt fine sand

17 GA0341 17 GA0341/17GR17 130 50.2724 -12 29.2307 12 Mini Van-veen Unsorted coarse gravel fine sand silt

18 GA0341 18 GA0341/18GR18 130 49.0080 -12 29.5702 19 Mini Van-veen Unsorted coarse gravel sand shell fragment

19 GA0341 19 GA0341/19GR19 130 48.1766 -12 29.2878 9 Mini Van-veen Reef

20 GA0341 20 130 47.8557 -12 25.9766 19 Mini Van-veen No sample return. Clean medium shell grit

21 GA0341 21 GA0341/21GR21 130 52.3963 -12 32.7060 13 Mini Van-veen Coarse gravel silt mud clasts sand shell grit



22 GA0341 22 GA0341/22GR22 130 52.0618 -12 32.2775 15 Mini Shipek Silt fine shell grit fine sand organics

23 GA0341 23 GA0341/23GR23 130 51.0027 -12 31.5488 17 Mini Shipek Silt coarse gravel shell grit fine sand

24 GA0341 24 GA0341/24GR24 130 51.2305 -12 32.4366 13 Mini Shipek Silt fine sand coarse shell fragment

25 GA0341 25 GA0341/25GR25 130 51.4275 -12 33.2542 15 Mini Shipek Coarse shell grit

26 GA0341 26 GA0341/26GR26 130 52.4253 -12 34.2445 15 Mini Shipek Silt coarse gravel fine sand fine shell grit

27 GA0341 27 GA0341/27GR27 130 51.3793 -12 34.7731 11 Mini Shipek Coarse poorly sorted gravel with silt and shell grit

28 GA0341 28 GA0341/28GR28 130 49.9979 -12 31.7349 16 Mini Shipek Mud fine grained shell grit medium gravel

29 GA0341 29 GA0341/29GR29 130 48.546 -12 26.795 23 Mini Shipek Camera Coarse shell grit unsorted gravel

30 GA0341 30 130 48.3022 -12 27.3713 30 Mini Shipek Camera No sample return. Large cobbles on silty sand

31 GA0341 31 GA0341/31GR31 130 46.3605 -12 27.7084 5 Mini Shipek Camera Fine sand silt

32 GA0341 32 GA0341/32GR32 130 46.3952 -12 27.2852 8 Mini Shipek Camera Soft very fine sand silt

33 GA0341 33 GA0341/33GR33 130 46.1580 -12 27.1606 8 Mini Shipek Camera Silt unsorted medium gravel shell grit

34 GA0341 34 130 46.4199 -12 26.1169 14 Mini Shipek Camera No sample return. Reef

35 GA0341 35 GA0341/35GR35 130 47.7525 -12 23.7061 12 Mini Shipek Camera Gravel fine sand fine shell grit

36 GA0341 36 GA0341/36GR36 130 48.4508 -12 23.1928 10 Mini Shipek Camera Silt fine shell grit fine sand

37 GA0341 37 GA0341/37GR37 130 47.6658 -12 22.0282 6 Mini Shipek Camera Very fine sand

38 GA0341 38 GA0341/38GR38 130 47.0383 -12 22.2988 18 Mini Shipek Camera Silt fine grained sand medium gravel

39 GA0341 39 GA0341/39GR39 130 46.7209 -12 22.5215 18 Mini Shipek Camera Unsorted fine medium shell grit

40 GA0341 40 GA0341/40GR40 130 44.6326 -12 22.0196 22 Mini Shipek Camera Silt fine shell grit

41 GA0341 41 GA0341/41GR41 130 44.1746 -12 21.5586 19 Mini Shipek Camera Silt fine shell grit medium gravel

42 GA0341 42 GA0341/42GR42 130 48.1766 -12 29.2878 8 Mini Shipek Camera Biological unsorted coarse gravel

43 GA0341 43 GA0341/43GR43 130 49.9865 -12 30.5365 23 Mini Shipek Camera Unsorted shell grit medium gravel silt

44 GA0341 44 GA0341/44GR44 130 49.1297 -12 30.4511 10 Mini Shipek Camera Unsorted gravel shell grit silt

45 GA0341 45 GA0341/45GR45 130 48.0597 -12 31.6563 20 Mini Shipek Camera Mud coarse gravel fine shell grit



46 GA0341 46 GA0341/46GR46 130 47.3109 -12 31.9756 10 Mini Shipek Camera Mud fine silt

47 GA0341 47 GA0341/47GR47 130 47.7816 -12 30.7929 7 Mini Shipek Camera Medium gravel shell grit silt

48 GA0341 48 GA0341/48GR48 130 47.0701 -12 30.0649 4 Mini Shipek Camera Medium unsorted gravel silt

49 GA0341 49 GA0341/49GR49 130 47.8294 -12 29.6880 2 Mini Shipek Camera Shell grit silt gravel

50 GA0341 50 GA0341/50GR50 130 46.9127 -12 29.2935 11 Mini Shipek Camera Silt mud

51 GA0341 51 GA0341/51GR51 130 45.1751 -12 24.7652 10 Mini Shipek Camera Unsorted coarse gravel shell grit

52 GA0341 52 GA0341/52GR52 130 46.1914 -12 24.8204 36 Mini Shipek Camera Shell fragments/grit coarse gravel

53 GA0341 53 GA0341/53GR53 130 42.6910 -12 21.0079 13 Mini Shipek Camera Shell grit shell fragments

54 GA0341 54 GA0341/54GR54 130 43.0255 -12 20.9526 15 Mini Shipek Camera Shell grit medium

55 GA0341 55 GA0341/55GR55 130 42.0185 -12 20.0526 17 Mini Shipek Camera Silt mud

56 GA0341 56 GA0341/56GR56 130 48.6044 -12 23.7080 9 Mini Shipek Camera Sand silt

57 GA0341 57 GA0341/57GR57 130 48.1644 -12 24.8609 15 Mini Shipek Camera Medium gravel mud

58 GA0341 58 GA0341/58GR58 130 46.9416 -12 24.5319 15 Mini Shipek Camera Coarse unsorted gravel (algae/coral/reef)

59 GA0341 59 GA0341/59GR59 130 47.4315 -12 27.1515 20 Mini Shipek Camera Gravel pebbles shell grit mud clasts

60 GA0341 60 GA0341/60GR60 130 48.3022 -12 27.3713 30 Mini Shipek Camera Pebbles shell grit (reef/coral)

61 GA0341 61 GA0341/61GR61 130 49.1381 -12 28.4150 31 Mini Shipek Camera Shell Bryzoan fragment pebble

62 GA0341 62 GA0341/62GR62 130 50.1661 -12 28.4216 28 Mini Shipek Camera Fine shell grit sand mud gravel

63 GA0341 63 GA0341/63GR63 130 50.0616 -12 29.9335 27 Mini Shipek Camera Fine shell grid

64 GA0341 64 GA0341/64GR64 130 50.2724 -12 29.2307 12 Mini Shipek Camera Shell grit medium gravel mud


Appendix B Summary Statistics of Sediment Samples

Appendix Table B.2 Summary statistics describing sediment samples collected from Darwin Harbour by Geoscience Australia in 2013 and by DLRM in a different survey (please refer to introduction for more information). Geographic locations have been corrected against the GPS locations obtained from the underwater video system. Those entries with suffix _DLRM were samples collected by DLRM.

Sample ID Grab type Longitude Latitude Depth (m) %Mud %Sand %Gravel Sand Fraction CaCO3 Mean grain size (m) Comments

GA0341/01GR01 Van-veen 130.819811 -12.447895 -4.44 41.15 58.85 0 65.45 116 No gravel fraction obtained. Small quantity of fine organic matter in the sand fraction

GA0341/02GR02 Van-veen 130.815331 -12.446277 -1.71 0.3 83.63 16.07 49.57 713

GA0341/03GR03 Van-veen 130.818963 -12.429444 -6.25 45.89 54.11 0 69.10 80 No gravel fraction obtained

GA0341/04GR04 Van-veen 130.822728 -12.434456 -4.92 30.68 69.32 0 68.66 105 No gravel fraction obtained

GA0341/05GR05 Van-veen 130.829662 -12.465012 -11.25 48.2 51.8 0 65.97 96 No gravel fraction obtained. Small quantity of fine organic matter in the sand fraction

GA0341/06GR06 Van-veen 130.859687 -12.471184 -4.34 38.54 59.18 2.28 64.76 129

GA0341/07GR07 Van-veen 130.931803 -12.499861 -5.79 12.84 54.06 33.1 25.17 381

GA0341/08GR08 Van-veen 130.936847 -12.512873 -9.59 40.38 58.13 1.49 21.01 144 Small quantity of fine organic matter in the sand fraction

GA0341/09GR09 Van-veen 130.925252 -12.507752 -5.05 No sub-sampling-Bag contained two pieces of coral rubble - Photo taken

GA0341/10GR10 Van-veen 130.924700 -12.501417 -6.68 0.18 91.49 8.34 22.48 680

GA0341/11GR11 Van-veen 130.906663 -12.510407 -8.44 63.28 31.6 5.12 14.76 39

GA0341/12GR12 Van-veen 130.885900 -12.508202 -10.56 85.72 14.15 0.13 0 39 Small quantity of fine organic matter in the sand fraction

GA0341/13GR13 Van-veen 130.873444 -12.496720 -14.53 11.8 36.02 52.17 36.72 479 Only enough sample for wet sieving

GA0341/14GR14 Van-veen 130.865104 -12.494723 -9.47 25.75 71.36 2.89 78.13 364

GA0341/15GR15 Van-veen 130.873112 -12.477567 -5.38 72 27.97 0.03 72.66 57 Small quantity of fine organic matter in the sand fraction

GA0341/16GR16 Van-veen 130.852503 -12.461337 -4.69 22.46 41.89 35.65 42.36 475 Some organic matter in the sand fraction

GA0341/17GR17 Van-veen 130.837874 -12.487179 -10.99 17.63 51.66 30.71 39.93 497

GA0341/18GR18 Van-veen 130.816801 -12.492836 -18.83 4.86 13.79 81.35 0 377

GA0341/19GR19 Van-veen 130.802943 -12.488131 -8.82 No sub-sampling-Bag contained one piece of coral rubble - Photo taken

GA0341/20GR20 Van-veen 130.797595 -12.432943 -20.21 No sample supplied

GA0341/21GR21 Van-veen 130.873272 -12.545100 -12.60 7.28 33.85 58.86 60.68 479 Very large stone in the gravel fraction.

GA0341/22GR22 Shipek 130.867696 -12.537959 -14.60 25.75 57.55 16.7 71.53 247

GA0341/23GR23 Shipek 130.850044 -12.525814 -16.96 19.18 44.44 36.38 57.73 485 Gravel fraction mostly coral rubble and containing a large stone

GA0341/24GR24 Shipek 130.853841 -12.540610 -11.94 21.35 65.31 13.34 34.98 198

GA0341/25GR25 Shipek 130.857126 -12.554237 -13.99 0.12 87.85 12.02 71.88 1053 Gravel and Sand fraction comprise ~99% coral/shell fragments

GA0341/26GR26 Shipek 130.873755 -12.570741 -14.27 18.09 39.02 42.89 46.61 219 Small amount of organic matter in Gravel fraction

GA0341/27GR27 Shipek 130.856321 -12.579552 -9.83 6.88 36.04 57.07 37.33 229 Very large stone in the gravel fraction. Sand fraction comprising mainly shell

GA0341/28GR28 Shipek 130.833298 -12.528916 -16.52 38.21 44.53 17.26 43.84 184 Small quantity of fine organic matter in the sand fraction

GA0341/29GR29 Shipek 130.809377 -12.446587 -23.76 0.56 58.45 41 63.72 16 Gravel and Sand fraction comprise ~99% coral/shell fragments

GA0341/30GR30 Shipek 130.804840 -12.456160 -31.78 No sample supplied

GA0341/31GR31 Shipek 130.772712 -12.461722 -5.06 34.35 65.65 0 63.28 122 No gravel fraction obtained

GA0341/32GR32 Shipek 130.773258 -12.454750 -8.52 57.06 42.56 0.38 69.44 87

GA0341/33GR33 Shipek 130.768972 -12.452468 -7.22 14.22 39.12 46.66 38.11 468 Very large stone in the gravel fraction.

GA0341/34GR34 Shipek 130.774195 -12.435578 -11.61 10.75 65.92 23.33 47.74

GA0341/35GR35 Shipek 130.795850 -12.395427 -11.97 1.71 43.68 54.61 44.44 382

GA0341/36GR36 Shipek 130.807453 -12.386618 -9.46 32.64 61.34 6.02 73.61 219

GA0341/37GR37 Shipek 130.794510 -12.367097 -5.19 4.65 95.35 0 64.84 200 No gravel fraction



GA0341/38GR38 Shipek 130.783658 -12.371712 -17.77 21.86 63.21 14.92 47.74 134

GA0341/39GR39 Shipek 130.778550 -12.375430 -17.28 0.94 87.56 11.51 68.14 746

GA0341/40GR40 Shipek 130.743712 -12.366672 -21.40 21.54 66.56 11.9 57.47 269

GA0341/41GR41 Shipek 130.736075 -12.358960 -18.07 24.35 64.63 11.01 54.95 363

GA0341/42GR42 Shipek 130.802957 -12.488312 -9.01 4.17 18.07 77.76 56.51 620 Coral Rubble-Sub-sample was taken for all analysis requirements - Photo taken

GA0341/43GR43 Shipek 130.832568 -12.509032 -24.64 9.89 48.65 41.46 62.07 554

GA0341/44GR44 Shipek 130.818843 -12.507712 -10.98 16.33 38.24 45.43 50.00 249

GA0341/45GR45 Shipek 130.801003 -12.527952 -21.68 28.14 25.51 46.35 40.63 196 Small amount of organic matter in Gravel fraction

GA0341/46GR46 Shipek 130.787773 -12.533012 -9.13 84.5 15.5 0 0 36 No gravel fraction obtained

GA0341/47GR47 Shipek 130.796135 -12.513403 -7.18 10.54 48.94 40.51 29.77 536

GA0341/48GR48 Shipek 130.784372 -12.501202 -3.91 1.95 51.26 46.8 30.47 808

GA0341/49GR49 Shipek 130.797012 -12.494950 -1.98 4.97 70.12 24.91 77.08 688


GA0341/51GR51 Shipek 130.752932 -12.412830 -8.79 0.33 37.7 61.98 28.65 776

GA0341/52GR52 Shipek 130.769942 -12.414173 -36.18 0.94 21.54 77.52 50.69 664

GA0341/53GR53 Shipek 130.709643 -12.349098 -15.04 0.12 84.55 15.33 67.53 868

GA0341/54GR54 Shipek 130.715998 -12.348448 -18.68 0.37 97.26 2.38 42.01 805


GA0341/56GR56 Shipek 130.810175 -12.395205 -10.17 41.31 53.58 5.11 72.74 151

GA0341/57GR57 Shipek 130.802565 -12.414407 -16.61 8.66 56.23 35.12 45.14 395

GA0341/58GR58 Shipek 130.782278 -12.409180 -16.88 0.7 9.91 89.39 0 152 Large stones in the gravel fraction

GA0341/59GR59 Shipek 130.790268 -12.452848 -21.99 1.79 0 98.21 41.75 616

GA0341/60GR60 Shipek 130.805900 -12.457615 -30.51 1.16 5.81 93.03 Very large stone in the gravel fraction.

GA0341/61GR61 Shipek 130.835967 -12.473770 -28.52 1.77 29.67 68.56 49.83 77

GA0341/62GR62 Shipek 130.834268 -12.499785 -26.99 27.59 32.07 40.34 39.15 288

GA0341/63GR63 Shipek 130.837950 -12.487172 -11.13 0.54 96.16 3.3 56.86 475

GA0341/64GR64 Shipek 130.837950 -12.487172 -11.13 18.44 62.64 18.92 42.36 492

GA0341/01GR01A_DRLM Van-veen 130.761408 -12.481775 74.13 25.73 0.14 73.18 76

GA0341/01GR01B_DRLM Van-veen 130.759922 -12.483483 63.38 36.62 0 72.31 75 No gravel fraction obtained

GA0341/02GR02A_DRLM Van-veen 130.768483 -12.473475 14.77 85.11 0.12 61.46 120

GA0341/02GR02B_DRLM Van-veen 130.768958 -12.471683 -3.19 29.42 70.2 0.39 54.69 211

GA0341/03GR03A_DRLM Van-veen 130.766722 -12.472306 -8.30 39.13 53.4 7.47 44.10 191












GA0341/08GR08A_DRLM Van-veen 130.784867 -12.474400 -18.52 No sample supplied

GA0341/08GR08B_DRLM Van-veen 130.785400 -12.474467 -19.69 No sample supplied







GA0341/12GR12A_DRLM Van-veen 130.797950 -12.471033 -26.66 No sample supplied

GA0341/12GR12B_DRLM Van-veen 130.797750 -12.470567 -27.66 No sample supplied


Appendix C Summary of Video-derived Substratum and Key Biological Composition

Appendix Table C.3 A summary of the underwater video-derived substratum and key biological composition for survey locations collected by GA only.

Sample ID Longitude Latitude Depth (m) %Rock %Boulder %Cobble %Pebble %Gravel %Sand %Mud Relief Bedform Veneer Dominant Biology

GA0341/29GR29 130.809377 -12.446587 -23.760000 0 0 0 0 50 50 0 Flat None no No visible biota

GA0341/30GR30 130.804840 -12.456160 -31.780000 20 0 30 0 20 20 10 Low/moderate (1-3 m) None yes Visible biota | Sponges

GA0341/31GR31 130.772712 -12.461722 -5.060000 0 0 0 0 0 20 80 Flat Bioturbated no No visible biota


GA0341/33GR33 130.768972 -12.452468 -7.220000 20 0 0 0 0 20 60 Flat None yes Visible biota | Cnidaria | Black & Octocorals

GA0341/34GR34 130.774195 -12.435578 -11.610000 50 0 0 0 30 10 10 Low/moderate (1-3 m) None yes Visible biota | Cnidaria | Black & Octocorals



GA0341/37GR37 130.794510 -12.367097 -5.190000 0 0 0 0 0 10 90 Flat 3D ripples/waves no No visible biota



GA0341/40GR40 130.743712 -12.366672 -21.400000 0 0 5 0 0 10 85 Flat Bioturbated yes Visible biota | Cnidaria | Stony corals







GA0341/47GR47 130.796135 -12.513403 -7.180000 0 0 0 10 20 10 60 Flat Bioturbated n No visible biota

GA0341/48GR48 130.784372 -12.501202 -3.910000 10 0 0 0 30 10 50 Low/moderate (1-3 m) Bioturbated yes Visible biota | Sponges









GA0341/57GR57 130.802565 -12.414407 -16.610000 10 0 0 0 20 40 30 Flat Bioturbated yes Visible biota | Sponges

GA0341/58GR58 130.782278 -12.409180 -16.880000 20 0 0 0 50 20 10 Flat None yes Visible biota | Cnidaria | Black & Octocorals


GA0341/60GR60 130.805900 -12.457615 -30.510000 0 0 80 0 0 0 20 Low/moderate (1-3 m) None no Visible biota | Cnidaria | Hydroids

GA0341/61GR61 130.835967 -12.473770 -28.520000 0 0 0 0 0 20 80 Unscorable None N/A Unscorable




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