Charting the Course for Future Developments in Marine Geomorphometry: An Introduction to the Special Issue

Abstract

The use of spatial analytical techniques for describing and classifying seafloor terrain has become increasingly widespread in recent years, facilitated by a combination of improved mapping technologies and computer power and the common use of Geographic Information Systems. Considering that the seafloor represents 71% of the surface of our planet, this is an important step towards understanding the Earth in its entirety. Bathymetric mapping systems, spanning a variety of sensors, have now developed to a point where the data they provide are able to capture seabed morphology at multiple scales, opening up the possibility of linking these data to oceanic, geological, and ecological processes. Applications of marine geomorphometry have now moved beyond the simple adoption of techniques developed for terrestrial studies. Whilst some former challenges have been largely resolved, we find new challenges constantly emerging from novel technology and applications. As increasing volumes of bathymetric data are acquired across the entire ocean floor at scales relevant to marine geosciences, resource assessment, and biodiversity evaluation, the scientific community needs to balance the influx of high-resolution data with robust quantitative processing and analysis techniques. This will allow marine geomorphometry to become more widely recognized as a sub-discipline of geomorphometry as well as to begin to tread its own path to meet the specific challenges that are associated with seabed mapping. This special issue brings together a collection of research articles that reflect the types of studies that are helping to chart the course for the future of marine geomorphometry.

Full text

geosciences
Editorial
Charting the Course for Future Developments in
Marine Geomorphometry: An Introduction to the
Special Issue
Vanessa Lucieer 1, * , Vincent Lecours 2and Margaret F. J. Dolan 3
1Institute for Marine and Antarctic Studies, University of Tasmania, Tasmania 7000, Australia
2
Fisheries & Aquatic Sciences | Geomatics, School of Forest Resources & Conservation, University of Florida,
Gainesville, FL 32653, USA; vlecours@ufl.edu
3Geological Survey of Norway (NGU), Postal Box 6315 Torgarden, NO-7491 Trondheim, Norway;
margaret.dolan@ngu.no
*Correspondence: vanessa.lucieer@utas.edu.au; Tel.: +61-3-6226-6931
Received: 7 December 2018; Accepted: 10 December 2018; Published: 13 December 2018


Abstract:
The use of spatial analytical techniques for describing and classifying seafloor terrain
has become increasingly widespread in recent years, facilitated by a combination of improved
mapping technologies and computer power and the common use of Geographic Information Systems.
Considering that the seafloor represents 71% of the surface of our planet, this is an important step
towards understanding the Earth in its entirety. Bathymetric mapping systems, spanning a variety
of sensors, have now developed to a point where the data they provide are able to capture seabed
morphology at multiple scales, opening up the possibility of linking these data to oceanic, geological,
and ecological processes. Applications of marine geomorphometry have now moved beyond the
simple adoption of techniques developed for terrestrial studies. Whilst some former challenges
have been largely resolved, we find new challenges constantly emerging from novel technology and
applications. As increasing volumes of bathymetric data are acquired across the entire ocean floor at
scales relevant to marine geosciences, resource assessment, and biodiversity evaluation, the scientific
community needs to balance the influx of high-resolution data with robust quantitative processing
and analysis techniques. This will allow marine geomorphometry to become more widely recognized
as a sub-discipline of geomorphometry as well as to begin to tread its own path to meet the specific
challenges that are associated with seabed mapping. This special issue brings together a collection of
research articles that reflect the types of studies that are helping to chart the course for the future of
marine geomorphometry.
Keywords:
bathymetry; digital terrain analysis; geomorphometry; geomorphology; habitat mapping;
marine remote sensing
1. Introduction
Geomorphometry (or digital terrain analysis, digital terrain modelling) is the science of
quantitative surface analysis [
1
,
2
] that evolved from mathematics, Earth sciences, and computer
science [
3
]. Geomorphometry developed its roots in geomorphology, but its branches reached out
to a variety of end-user disciplines, such as the environmental sciences, space exploration, and civil
engineering [
4
]. The widespread integration of geomorphometric tools into Geographic Information
Systems (GIS) made geomorphometric analyses accessible to a wide range of end-users, many of
whom are not necessarily aware of the science underpinning the tools [
5
]. Over the last decade,
efforts have been made (e.g., [
6
–
13
]) to bridge the gap between the discipline of geomorphometry,
which has traditionally focused on terrestrial and planetary applications, and the marine sciences.
Geosciences 2018,8, 477; doi:10.3390/geosciences8120477 www.mdpi.com/journal/geosciences

Geosciences 2018,8, 477 2 of 9
Those efforts have resulted in the broader geomorphometry community becoming more aware of
the challenges specific to the analysis of seafloor bathymetry data. Over the same period, the marine
sciences community has become more aware of the field of geomorphometry, although some of its
concepts, tools, and applications are still perhaps more widely recognized than others.
This special issue on Marine Geomorphometry is timely, as mutual recognition by the
geomorphometry and marine sciences communities is higher than ever. The need to work together to
solve emerging issues in quantitative seafloor analysis is also increasingly being acknowledged by
both communities. This special issue explores existing and emerging trends where marine science
applications and geomorphometry meet. As technology takes us deeper into the oceans and reveals
its landscapes at increasingly higher resolution, there is much to learn about the seafloor. At the
same time, it is important for the scientific community to show some restraint and critically assess
the methods applied to analyze and explain these seafloor environments. Through this special issue,
the community is demonstrating this restraint by questioning, assessing, and discussing the spatial
geomorphometric techniques that they are applying to their seafloor data. The 17 papers in this issue
address the five fundamental steps for implementing geomorphometric analysis, and these steps allow
us to expose many of the important lessons learned to address various challenges. By continuing
research along these lines, we will ensure that, as new characterization methods are developed, they
are valid, repeatable, and robust to classification ontologies.
2. Five Steps to Implement Geomorphometric Analyses
The complete geomorphometry workflow involves five main steps [
3
]: sampling a surface,
generating a digital terrain model (DTM) from the sampled surface, preprocessing the DTM
(
e.g., correcting
for errors) for subsequent analyses, deriving terrain attributes and/or extracting
terrain features from the DTM, and using and explaining those attributes and features in a given
context. Early applications of marine geomorphometry often only focused on the analysis of the
digital bathymetric model (DBM) and its application, disregarding the importance of the first three
steps [
7
]. Over the last few years, however, end-user awareness about the impacts of the earlier steps
of the geomorphometry workflow on applications has significantly increased [
10
]. The articles in this
special issue highlight this trend, and Sections 2.1–2.5 summarize the contributions to this special issue
according to each of those five steps.
2.1. Sampling the Depth of the Seafloor
While acoustic remote sensing technologies remain the main tools used to sample the depth
and composition of the seafloor (Figure 1), the challenges that are associated with using them in
very shallow waters have long made the coastal environment one of the most difficult in which to
collect depth information. However, optical remote sensing technologies, such as bathymetric lidar
and multispectral satellite imagery, are slowly gaining traction in coastal applications due to recent
developments in hardware and processing methods. For instance, Walbridge et al. [
14
] used a 3 m
resolution lidar dataset of the Buck Island Reef National Monument, in the U.S. Virgin Islands, to
classify the seafloor into nine geomorphic classes. Linklater et al. [
15
] empirically derived depth
estimates from 2-m resolution WorldView-2 and 2.4-m resolution Quickbird satellite images that were
corrected for atmospheric effects and sun glint. Those depth estimates were then combined with
existing acoustic data from deeper waters to develop a seamless, high-resolution DBM of the shelf
around Lord Howe Island (Southwest Pacific Ocean), from which geomorphometric analyses were
performed. The use of optical remote sensing in marine geomorphometry is likely to increase in
the next few years as both empirical [
16
] and physical [
17
] approaches for bathymetric derivation
become more widely available in user-friendly tools (e.g., Traganos et al., [
18
]) and new techniques
(e.g., satellite-derived photogrammetric bathymetry, see [19]) are developed.

Geosciences 2018,8, 477 3 of 9
Geosciences 2018, 8, x FOR PEER REVIEW 3 of 10
Figure 1. The techniques used in the special issue to sample seafloor depths. Some articles combined
multiple techniques. The category ‘other existing data’ includes, for example, navigational charts.
The use of radar altimetry to estimate depth has declined over the years due to its inability to
capture seafloor morphology at scales relevant to many applications. Recognizing the poor state of
global single-resolution ocean depth maps and the critical role that such knowledge plays in
understanding our planet, the International Hydrographic Organization-Intergovernmental
Oceanographic Commission (IHO-IOC) General Bathymetric Chart of the Oceans (GEBCO;
information available at www.gebco.net) framework and the Nippon Foundation have joined forces
to establish the Nippon Foundation-GEBCO Seabed 2030 Project. This represents an international
effort with the objective of facilitating the complete mapping of the world’s oceans by 2030. The
concept paper by Mayer et al. [20] outlines the ambitious Seabed 2030 initiative and the possibilities
that these global data will bring to users worldwide and that will only be seen in the years to come.
Methods to process these data will indeed require the support of the Global Data Assembly and
Coordination Centre (GDACC), who will likely turn to the research community for efficient and
robust spatial-data-processing methods to extract common and valuable variables of interest to the
international marine community. This special issue provides a relevant summary of these methods
(cf. Section 2.4) and the applications of these to multidisciplinary research (cf. Section 2.5).
In managing the oceans, it is widely recognized that one must first acknowledge their nature as
a system, and in the large marine ecosystem paradigm, our perception of it is influenced by the scales
at which they are examined, which are directly dependent on the sampling technique. Figure 2 shows
that applications presented in this special issue looked at geosystems and ecosystems at a wide range
of spatial scales. Finer-resolution data were most often produced by optical means (i.e., lidar and
satellite imagery) and by acoustic means in shallower waters, while broader-resolution data were
produced by acoustic methods in deeper waters and by using datasets with sparse coverage (cf.
Section 2.2).
Figure 1.
The techniques used in the special issue to sample seafloor depths. Some articles combined
multiple techniques. The category ‘other existing data’ includes, for example, navigational charts.
The use of radar altimetry to estimate depth has declined over the years due to its inability
to capture seafloor morphology at scales relevant to many applications. Recognizing the poor
state of global single-resolution ocean depth maps and the critical role that such knowledge
plays in understanding our planet, the International Hydrographic Organization-Intergovernmental
Oceanographic Commission (IHO-IOC) General Bathymetric Chart of the Oceans (GEBCO; information
available at www.gebco.net) framework and the Nippon Foundation have joined forces to establish
the Nippon Foundation-GEBCO Seabed 2030 Project. This represents an international effort with the
objective of facilitating the complete mapping of the world’s oceans by 2030. The concept paper by
Mayer et al. [
20
] outlines the ambitious Seabed 2030 initiative and the possibilities that these global data
will bring to users worldwide and that will only be seen in the years to come. Methods to process these
data will indeed require the support of the Global Data Assembly and Coordination Centre (GDACC),
who will likely turn to the research community for efficient and robust spatial-data-processing methods
to extract common and valuable variables of interest to the international marine community. This
special issue provides a relevant summary of these methods (cf. Section 2.4) and the applications of
these to multidisciplinary research (cf. Section 2.5).
In managing the oceans, it is widely recognized that one must first acknowledge their nature as a
system, and in the large marine ecosystem paradigm, our perception of it is influenced by the scales at
which they are examined, which are directly dependent on the sampling technique. Figure 2shows
that applications presented in this special issue looked at geosystems and ecosystems at a wide range of
spatial scales. Finer-resolution data were most often produced by optical means (
i.e., lidar
and satellite
imagery) and by acoustic means in shallower waters, while broader-resolution data were produced by
acoustic methods in deeper waters and by using datasets with sparse coverage (cf. Section 2.2).
Geosciences 2018, 8, x FOR PEER REVIEW 4 of 10
Figure 2. The range of resolutions presented or discussed in the articles.
2.2. Generating a Digital Bathymetric Model
Methods to generate DBMs have not significantly changed in the last few years. For example,
many studies, including [21], still use the CUBE (Combined Uncertainty and Bathymetric Estimator)
algorithm [22] to grid multibeam echosounder data. Given the costs that are associated with
collecting bathymetric data over large areas of seafloor, there has also been a growing interest in
using data fusion to combine existing data from multiple sources. Zimmermann and Prescott [23]
accomplished the feat of combining 18 million data points from more than 200 individual sources to
produce the best bathymetric model to date of the Eastern Bering Sea, enabling them to study 29
canyons in the area and to confirm the legendary status of some pinnacles. Bourguignon et al. [24]
used single-beam echosounders data and chart data to produce a 200-m resolution DBM by
interpolating more than 150,000 points.
In some cases, the costs of data collection are not the only impediments to the production of
DBMs, but our inability to travel through time may be: Goswami et al. [25] presented an innovative
approach to produce a DBM representing a reconstruction of paleobathymetry from 94 Ma, with
implications for paleoclimate studies, among others. This new bathymetric model at 0.1° × 0.1°
resolution improves upon present global paleoclimate simulation model layers that are developed
from bathtub-like, flat, featureless ocean bathymetry models, which are neither realistic nor suitable.
This approach represents an important step forward for this type of application.
2.3. Preprocessing
Unlike in terrestrial applications of geomorphometry, for which DTMs need to be hydrologically
corrected (e.g., by removing sinks), the preparation of DBMs for marine applications mainly consists
in correcting errors and artefacts that could not be accounted for during the processing of raw data
to generate the DBM or filling in data gaps to facilitate analyses and reduce potential edge effects.
For instance, Porskamp et al. [26] used Delaunay triangulation to stitch multiple datasets and fill any
holes in the final product.
While simple methods to correct for different types of artefacts in DBMs are still lacking, the
awareness of artefacts and their potential impacts on applications is now regularly acknowledged
and reported on (e.g., Ryabchuck et al. [27]), which used to be very uncommon [28,29]. In this special
issue, Hughes Clarke [30] addresses the main factors that affect data quality in bathymetric data
collected using multibeam echosounders. Multibeam acoustic swath systems are the common
instrument of choice for a full-coverage bathymetric survey. Within each swath of data, the variables
Figure 2. The range of resolutions presented or discussed in the articles.

Geosciences 2018,8, 477 4 of 9
2.2. Generating a Digital Bathymetric Model
Methods to generate DBMs have not significantly changed in the last few years. For example,
many studies, including [
21
], still use the CUBE (Combined Uncertainty and Bathymetric Estimator)
algorithm [
22
] to grid multibeam echosounder data. Given the costs that are associated with collecting
bathymetric data over large areas of seafloor, there has also been a growing interest in using data
fusion to combine existing data from multiple sources. Zimmermann and Prescott [
23
] accomplished
the feat of combining 18 million data points from more than 200 individual sources to produce the
best bathymetric model to date of the Eastern Bering Sea, enabling them to study 29 canyons in the
area and to confirm the legendary status of some pinnacles. Bourguignon et al. [
24
] used single-beam
echosounders data and chart data to produce a 200-m resolution DBM by interpolating more than
150,000 points.
In some cases, the costs of data collection are not the only impediments to the production of DBMs,
but our inability to travel through time may be: Goswami et al. [
25
] presented an innovative approach
to produce a DBM representing a reconstruction of paleobathymetry from 94 Ma, with implications for
paleoclimate studies, among others. This new bathymetric model at 0.1
◦×
0.1
◦
resolution improves
upon present global paleoclimate simulation model layers that are developed from bathtub-like, flat,
featureless ocean bathymetry models, which are neither realistic nor suitable. This approach represents
an important step forward for this type of application.
2.3. Preprocessing
Unlike in terrestrial applications of geomorphometry, for which DTMs need to be hydrologically
corrected (e.g., by removing sinks), the preparation of DBMs for marine applications mainly consists
in correcting errors and artefacts that could not be accounted for during the processing of raw data
to generate the DBM or filling in data gaps to facilitate analyses and reduce potential edge effects.
For instance
, Porskamp et al. [
26
] used Delaunay triangulation to stitch multiple datasets and fill any
holes in the final product.
While simple methods to correct for different types of artefacts in DBMs are still lacking,
the awareness
of artefacts and their potential impacts on applications is now regularly acknowledged
and reported on (e.g., Ryabchuck et al. [
27
]), which used to be very uncommon [
28
,
29
]. In this
special issue, Hughes Clarke [
30
] addresses the main factors that affect data quality in bathymetric
data collected using multibeam echosounders. Multibeam acoustic swath systems are the common
instrument of choice for a full-coverage bathymetric survey. Within each swath of data, the variables
of distance, azimuth, and elevation angles will influence significantly the quality of the data.
This variability will translate through to the DBM and subsequent users, if unfamiliar with the
original acquisition geometry, may potentially misinterpret such variability as real attributes on the
seabed, particularly if the artefacts are at the same scale as the morphologic features of interest [
31
].
Hughes Clarke [
30
] warns of the uncertainty that can arise with the ever-increasing ambition of
higher-resolution data and cautions that relief close to either the resolution limit or the scale of artefacts
increases the risk of over-interpretation by morphological studies.
2.4. Analysing the Digital Bathymetric Model
There has not been much change in terms of general geomorphometry (which focuses on the
derivation of terrain attributes) since the reviews by Lecours et al. [
7
,
10
]. As identified in Figure 3,
slope remains the most commonly used terrain attribute, followed by measures of curvature, rugosity,
and topographic position. Tools to automatically compute and analyze those measures are, however,
increasingly being developed and made available to the broader community (see examples in [
7
]).
In this special issue
, Walbridge et al. [
14
] offers a review of such tools and toolboxes and presents the
most recent developments to their Benthic Terrain Modeler (BTM) toolbox.

Geosciences 2018,8, 477 5 of 9
Geosciences 2018, 8, x FOR PEER REVIEW 5 of 10
of distance, azimuth, and elevation angles will influence significantly the quality of the data. This
variability will translate through to the DBM and subsequent users, if unfamiliar with the original
acquisition geometry, may potentially misinterpret such variability as real attributes on the seabed,
particularly if the artefacts are at the same scale as the morphologic features of interest [31]. Hughes
Clarke [30] warns of the uncertainty that can arise with the ever-increasing ambition of higher-
resolution data and cautions that relief close to either the resolution limit or the scale of artefacts
increases the risk of over-interpretation by morphological studies.
2.4. Analysing the Digital Bathymetric Model
There has not been much change in terms of general geomorphometry (which focuses on the
derivation of terrain attributes) since the reviews by Lecours et al. [7,10]. As identified in Figure 3,
slope remains the most commonly used terrain attribute, followed by measures of curvature,
rugosity, and topographic position. Tools to automatically compute and analyze those measures are,
however, increasingly being developed and made available to the broader community (see examples
in [7]). In this special issue, Walbridge et al. [14] offers a review of such tools and toolboxes and
presents the most recent developments to their Benthic Terrain Modeler (BTM) toolbox.
Figure 3. The categories of terrain attributes used in the articles of the special issue.
Specific geomorphometry, i.e., the branch of geomorphometry that deals with the extraction of
terrain objects/features, is also well-represented in this special issue. Di Stefano and Mayer [21]
developed a scale-based model for extracting and quantifying characteristics of submarine landforms
(mainly sand dunes, ripples, mega ripples, and coral reefs) from high-resolution digital bathymetry.
Their approach follows a two-part procedure wherein the first part the model extracts terrain features
based on differential geometry principles and the second part evaluates the models for their
relationships to scale-dependency, simulating their sensitivity to variation in the input parameters.
Diesing and Thorsnes [32] present a methodology that combines image segmentation and random
forest spatial prediction with the aim to derive maps of cold-water coral carbonate mounds with
associated, spatially explicit measures of confidence. This approach is successful in mapping the
presence and absence of carbonate mounds with high accuracy and confidence and shows promise
for more widespread application. The variables used to facilitate carbonate mound detection include
curvature, roughness, length, width, and bathymetric position index, demonstrating how general
geomorphometry underpins further applied analysis and modelling. Finally, Masetti et al. [33]
adapted the geomorphons concept introduced by Jasiewicz and Stepinski [34] for terrestrial and
planetary settings to make it more meaningful for the study of marine bedforms. The identified
“bathymorphons”, a term used by the authors, provide a robust and flexible way to segment acoustic
Figure 3. The categories of terrain attributes used in the articles of the special issue.
Specific geomorphometry, i.e., the branch of geomorphometry that deals with the extraction
of terrain objects/features, is also well-represented in this special issue. Di Stefano and Mayer [
21
]
developed a scale-based model for extracting and quantifying characteristics of submarine landforms
(mainly sand dunes, ripples, mega ripples, and coral reefs) from high-resolution digital bathymetry.
Their approach follows a two-part procedure wherein the first part the model extracts terrain
features based on differential geometry principles and the second part evaluates the models for their
relationships to scale-dependency, simulating their sensitivity to variation in the input parameters.
Diesing and Thorsnes [
32
] present a methodology that combines image segmentation and random
forest spatial prediction with the aim to derive maps of cold-water coral carbonate mounds with
associated, spatially explicit measures of confidence. This approach is successful in mapping the
presence and absence of carbonate mounds with high accuracy and confidence and shows promise
for more widespread application. The variables used to facilitate carbonate mound detection include
curvature, roughness, length, width, and bathymetric position index, demonstrating how general
geomorphometry underpins further applied analysis and modelling. Finally, Masetti et al. [
33
]
adapted the geomorphons concept introduced by Jasiewicz and Stepinski [
34
] for terrestrial and
planetary settings to make it more meaningful for the study of marine bedforms. The identified
“bathymorphons”, a term used by the authors, provide a robust and flexible way to segment acoustic
seafloor data based on principles of topographic openness, pattern recognition, texture classification,
object similarity, and multi-modality.
2.5. Applications
In line with the review by Lecours et al. [
10
], the two main applications of marine geomorphometry
remain in the general fields of geomorphology and habitat mapping (Figure 4). While Figure 4shows
a stronger representation of geomorphology, we note that the publisher for this special issue might
have influenced the relative proportions of submissions from each field. Gardner [
35
] studied the
Mendocino Channel, a deep-water sinuous channel, and quantitatively described its morphology and
structural maintenance. The author asserts that the formation, maintenance, and modification of the
Mendocino Channel have occurred through a combination of significant and numerous earthquakes
and wave loading resuspension by storms forming turbidity currents. Ryabchuk et al. [27] used both
a multibeam echosounder and a sub-bottom profiler to identify and map submerged glacial and
post-glacial geomorphological features, enabling them to interpret the sedimentation regimes of two
post-glacial basins in the Gulf of Finland. The geomorphological analysis has led to the identification
of Late Pleistocene sediment and more modern bottom relief, which together indicated the occurrence

Geosciences 2018,8, 477 6 of 9
of a deep-water level fall in the Early Holocene and multiple water-level fluctuations during this
period. Also, in this special issue, Gafeira et al. [
36
] introduced a semi-automated approach to spatially
delineate pockmarks of different shapes and sizes in different geological settings. Their approach
proved to be less subjective and faster than traditional methods, such as manual expert identification
and delineation. Sánchez-Guillamón et al. [
37
] used morphometry and size to classify deep seafloor
mounds, such as domes and volcanoes, in the Canary Basin and proposed a growth model of those
mounds informed by their geomorphometric characteristics.
Geosciences 2018, 8, x FOR PEER REVIEW 6 of 10
seafloor data based on principles of topographic openness, pattern recognition, texture classification,
object similarity, and multi-modality.
2.5. Applications
In line with the review by Lecours et al. [10], the two main applications of marine
geomorphometry remain in the general fields of geomorphology and habitat mapping (Figure 4).
While Figure 4 shows a stronger representation of geomorphology, we note that the publisher for
this special issue might have influenced the relative proportions of submissions from each field.
Gardner [35] studied the Mendocino Channel, a deep-water sinuous channel, and quantitatively
described its morphology and structural maintenance. The author asserts that the formation,
maintenance, and modification of the Mendocino Channel have occurred through a combination of
significant and numerous earthquakes and wave loading resuspension by storms forming turbidity
currents. Ryabchuk et al. [27] used both a multibeam echosounder and a sub-bottom profiler to
identify and map submerged glacial and post-glacial geomorphological features, enabling them to
interpret the sedimentation regimes of two post-glacial basins in the Gulf of Finland. The
geomorphological analysis has led to the identification of Late Pleistocene sediment and more
modern bottom relief, which together indicated the occurrence of a deep-water level fall in the Early
Holocene and multiple water-level fluctuations during this period. Also, in this special issue, Gafeira
et al. [36] introduced a semi-automated approach to spatially delineate pockmarks of different shapes
and sizes in different geological settings. Their approach proved to be less subjective and faster than
traditional methods, such as manual expert identification and delineation. Sánchez-Guillamón et al.
[37] used morphometry and size to classify deep seafloor mounds, such as domes and volcanoes, in
the Canary Basin and proposed a growth model of those mounds informed by their
geomorphometric characteristics.
Figure 4. The categories of applications that were presented in the special issue. Some articles had
multiple applications; for instance, when the geomorphology was interpreted and then used to map
habitats.
It is also noteworthy that many studies have both a geomorphological focus and a habitat-
mapping focus that complement each other. For instance, Greene et al. [38] studied deep-water sand
wave fields in the San Juan Archipelago of the Salish Sea, which form habitat for Pacific sand lances
and sand-eels. Of note, their interpretation of the features and habitats also considers the complex
hydrodynamics of the area. Linklater et al. [15] examined and compared reef morphology around the
subtropical island shelves of Lord Howe Island and Balls Pyramid in the Southwest Pacific Ocean for
the first time. Diverse accretionary and erosional geomorphic features were mapped, with highlights
Figure 4.
The categories of applications that were presented in the special issue. Some articles
had multiple applications; for instance, when the geomorphology was interpreted and then used to
map habitats.
It is also noteworthy that many studies have both a geomorphological focus and a habitat-mapping
focus that complement each other. For instance, Greene et al. [
38
] studied deep-water sand wave fields
in the San Juan Archipelago of the Salish Sea, which form habitat for Pacific sand lances and sand-eels.
Of note, their interpretation of the features and habitats also considers the complex hydrodynamics
of the area. Linklater et al. [
15
] examined and compared reef morphology around the subtropical
island shelves of Lord Howe Island and Balls Pyramid in the Southwest Pacific Ocean for the first
time. Diverse accretionary and erosional geomorphic features were mapped, with highlights including
fossil reef systems dominating the shelves in 25–50 m water depth. A geomorphological analysis
was used to provide insight into the geological and ecological processes that have influenced the
formation of these shelves around the two islands. Bourguignon et al. [
24
] examined the use of seabed
geomorphology and sedimentology to study the influence of sedimentary regimes on physical marine
habitat distribution. They then used that information to define potential fishing grounds and predict
fishing activities. The results are to be used for marine spatial planning on the Eastern Brazilian Shelf.
Picard et al. [
39
] supported this theme with a study of hydrodynamics patterns by documenting the
use of semi-automated methods to map and quantify the form and density of pockmark fields in one
of the regions with the highest concentration of those features in the world: the Northwest Australian
continental shelf. Whilst regional bi-directionality of pockmark scours corresponded to the modelled
tidal flow, localized scattering around banks suggested turbulence regimes. The geomorphological
analysis of these data proposed that pockmark scours can act as a proxy for bottom currents, which
could help to inform modelling of benthic biodiversity patterns.

Geosciences 2018,8, 477 7 of 9
3. Discussion
Geomorphometric analysis continues to evolve across each of the five themes mentioned in this
paper. There are several questions in seafloor quantitative characterization research that will occupy
our attention for decades to come and for which this special issue may progress discussion. There is a
complex interplay where new developments in this field will ebb between improved data collection and
data-processing technique to create higher-resolution and more accurate DBMs and workflows with
improved big data processing algorithms to handle larger and more complex automated methodologies
for feature extraction. As the paper by Mayer et al. [
20
] rightly points out, new approaches to seafloor
mapping will particularly enhance efficiency and coverage. However, as Hughes-Clarke [
30
] identifies,
analysts unfamiliar with acquisition geometry may potentially misinterpret variability in the data as
geomorphometric features, and similarly, sparse depth soundings can lead to a false impression of
flat seabed terrain. Bathymetric coverage of the seabed at various resolutions builds up the quest for
robust methods for the production and analysis of multiscale DBMs, which perhaps will become the
next major demand for marine geomorphometry. Methods for multiscale grid structure applied to
bathymetric data have been recently explored by Maleika et al. [
40
], whilst options for the generation of
multiresolution surfaces are now available in some multibeam processing software [
41
]. Further down
the line, data end-users now have the option to merge datasets at multiple resolutions and use these
directly in an analysis through data management solutions, such as the ESRI
®
Mosaic Dataset. It is
essential for the future integrity of marine geomorphometry that these various types of multiresolution
DBMs are produced and analyzed with due regard for the additional complexities of multiresolution
surfaces, supported by adequate documentation to make the methods transparent and verifiable. It
seems likely that terrain analysis methods focused on an analysis of distance rather than pixels will
become more applicable in providing suitable outputs from multiresolution surfaces.
Seafloor mapping is inherently a multidisciplinary task—a mix of hydrography, computer science,
engineering, physics, and mathematics—that also delivers valuable data to many more disciplines,
such as marine geology, oceanography, biology, habitat and species prediction modelling, remote
sensing, and hydrographic surveying. New applications for marine geomorphometry will continue to
be discovered as high-resolution data and marine geomorphometry becomes valued by even more
applications, such as seafloor energy harvesting, marine archeology, and deep-sea resource assessment.
Any new application areas will bring with them new challenges to marine geomorphometric analysis,
which can best be met through a strong partnership between those advancing marine remote sensing
and those developing geospatial techniques. We hope that this special issue identifies a breadth
of perspectives and integrates ideas that will help to further establish the discipline of marine
geomorphometry and provide the conduit to solve these future challenges.
Author Contributions:
V.L. (Vanessa Lucieer), V.L. (Vincent Lecours), and M.F.J.D. conceived and designed the
special review. V.L. (Vanessa Lucieer), V.L. (Vincent Lecours), and M.F.J.D. equally contributed to the writing of
this paper.
Funding:
This work was supported by the National Environmental Science Program (NESP) Marine Biodiversity
Hub at the University of Tasmania (Lucieer), the University of Florida (Lecours) and the Geological Survey of
Norway (Dolan).
Acknowledgments:
We wish to thank all the authors and co-authors who published in this special issue, and the
reviewers that have contributed to the success of this collection of high-quality and broad impact research.
Conflicts of Interest: The authors declare no conflict of interest.
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