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Geomorphometry, the science of quantitative terrain characterization, has traditionally focused on the investigation of terrestrial landscapes. However, the dramatic increase in the availability of digital bathymetric data and the increasing ease by which geomorphometry can be investigated using geographic information systems (GISs) and spatial analysis software has prompted interest in employing geomorphometric techniques to investigate the marine environment. Over the last decade or so, a multitude of geomorphometric techniques (e.g. terrain attributes, feature extraction, automated classification) have been applied to characterize seabed terrain from the coastal zone to the deep sea. Geomorphometric techniques are, however, not as varied, nor as extensively applied, in marine as they are in terrestrial environments. This is at least partly due to difficulties associated with capturing, classifying, and validating terrain characteristics underwater. There is, nevertheless, much common ground between terrestrial and marine geomorphometry applications and it is important that, in developing marine geomorphometry, we learn from experiences in terrestrial studies. However, not all terrestrial solutions can be adopted by marine geomorphometric studies since the dynamic, four-dimensional (4-D) nature of the marine environment causes its own issues throughout the geomorphometry workflow. For instance, issues with underwater positioning, variations in sound velocity in the water column affecting acoustic-based mapping, and our inability to directly observe and measure depth and morphological features on the seafloor are all issues specific to the application of geomorphometry in the marine environment. Such issues fuel the need for a dedicated scientific effort in marine geomorphometry. This review aims to highlight the relatively recent growth of marine geomorphometry as a distinct discipline, and offers the first comprehensive overview of marine geomorphometry to date. We address all the five main steps of geomorphometry, from data collection to the application of terrain attributes and features. We focus on how these steps are relevant to marine geomorphometry and also highlight differences and similarities from terrestrial geomorphometry. We conclude with recommendations and reflections on the future of marine geomorphometry. To ensure that geomorphometry is used and developed to its full potential, there is a need to increase awareness of (1) marine geomorphometry amongst scientists already engaged in terrestrial geomorphometry, and of (2) geomorphometry as a science amongst marine scientists with a wide range of backgrounds and experiences.
Examples of errors and artefacts found in different data sets and their impact on derived terrain attributes. The top panels represent data from GEBCO (2014), which uses radar altimetry to fill in the gaps between higher-resolution, freely available bathymetric data. The main artefacts that can be observed are caused by the interpolation method that was used to combine the different data sets. For instance, a linear artefact following a southwest to northeast axis can be observed as a result of the combination of one SBES acoustic survey line with radar altimetry data. Similarly, some " spots " can be seen in the middle of the panels (south to north direction). These artefacts, especially apparent in the curvature, are caused by the merging of punctual lead line measurements with the radar altimetry data. Finally, a slight gridding artefact can be observed in the curvature (i.e. thin vertical and horizontal linear features). The middle panels show ship-based MBES data (Brown et al., 2012). The obvious artefacts follow the surveying pattern of the vessel, and are mainly caused by vessel motion that was not compensated properly by the motion sensor. Finally, the bottom row of panels corresponds to ROV-based MBES collected from 20 m above the seafloor in the deep sea (Lecours et al., 2013). In this case, the artefacts are caused by a combination of heave and other platform motions; the ancillary data collected to account for this motion are too uncertain at this depth to appropriately correct for the errors (Lecours and Devillers, 2015). The " spots " observed in the bottom and top of the derived terrain attributes are spurious soundings that can be removed in bathymetric software during post-processing of the data. Note differences in spatial resolutions (left axis) and cartographic scales. Depth values of the top left panel range from 60 to 4275 m deep, those of the middle left panel range from 20 to 105 m deep, and those of the bottom left panel range from 2345 to 2425 m deep. Lighter blue is shallower.
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Hydrol. Earth Syst. Sci., 20, 3207–3244, 2016
© Author(s) 2016. CC Attribution 3.0 License.
A review of marine geomorphometry, the quantitative
study of the seafloor
Vincent Lecours1, Margaret F. J. Dolan2, Aaron Micallef3, and Vanessa L. Lucieer4
1Marine Geomatics Research Lab, Department of Geography, Memorial University of Newfoundland,
St. John’s, A1B 3X9, Canada
2Geological Survey of Norway, P.O. Box 6315 Sluppen, 7491 Trondheim, Norway
3Marine Geology and Seafloor Surveying, Department of Geosciences, University of Malta, Msida, MSD 2080, Malta
4Institute for Marine and Antarctic Studies, University of Tasmania, Hobart, 7004, Australia
Correspondence to: Vincent Lecours (
Received: 12 February 2016 – Published in Hydrol. Earth Syst. Sci. Discuss.: 1 March 2016
Revised: 4 July 2016 – Accepted: 17 July 2016 – Published: 9 August 2016
Abstract. Geomorphometry, the science of quantitative ter-
rain characterization, has traditionally focused on the inves-
tigation of terrestrial landscapes. However, the dramatic in-
crease in the availability of digital bathymetric data and the
increasing ease by which geomorphometry can be investi-
gated using geographic information systems (GISs) and spa-
tial analysis software has prompted interest in employing ge-
omorphometric techniques to investigate the marine environ-
ment. Over the last decade or so, a multitude of geomorpho-
metric techniques (e.g. terrain attributes, feature extraction,
automated classification) have been applied to characterize
seabed terrain from the coastal zone to the deep sea. Geo-
morphometric techniques are, however, not as varied, nor as
extensively applied, in marine as they are in terrestrial en-
vironments. This is at least partly due to difficulties associ-
ated with capturing, classifying, and validating terrain char-
acteristics underwater. There is, nevertheless, much common
ground between terrestrial and marine geomorphometry ap-
plications and it is important that, in developing marine geo-
morphometry, we learn from experiences in terrestrial stud-
ies. However, not all terrestrial solutions can be adopted by
marine geomorphometric studies since the dynamic, four-
dimensional (4-D) nature of the marine environment causes
its own issues throughout the geomorphometry workflow.
For instance, issues with underwater positioning, variations
in sound velocity in the water column affecting acoustic-
based mapping, and our inability to directly observe and
measure depth and morphological features on the seafloor
are all issues specific to the application of geomorphome-
try in the marine environment. Such issues fuel the need for
a dedicated scientific effort in marine geomorphometry.
This review aims to highlight the relatively recent growth
of marine geomorphometry as a distinct discipline, and offers
the first comprehensive overview of marine geomorphometry
to date. We address all the five main steps of geomorphome-
try, from data collection to the application of terrain attributes
and features. We focus on how these steps are relevant to ma-
rine geomorphometry and also highlight differences and sim-
ilarities from terrestrial geomorphometry. We conclude with
recommendations and reflections on the future of marine ge-
omorphometry. To ensure that geomorphometry is used and
developed to its full potential, there is a need to increase
awareness of (1) marine geomorphometry amongst scien-
tists already engaged in terrestrial geomorphometry, and of
(2) geomorphometry as a science amongst marine scientists
with a wide range of backgrounds and experiences.
1 Introduction
1.1 Background
Studies of geomorphology have improved our understanding
of many of the Earth’s systems and surface processes (Smith
et al., 2011; Bishop et al., 2012). Morphology and quantita-
tive measures of topography are considered the most impor-
tant components of geomorphology because they represent
the age and origin of the landscape (Speight, 1974; Minár and
Published by Copernicus Publications on behalf of the European Geosciences Union.
3208 V. Lecours et al.: A review of marine geomorphometry
Evans, 2008; Bishop et al., 2012). The shapes of the terres-
trial landscape are important for many Earth systems across
a range of scales. For instance, broadscale features such as
mountains and valleys may dictate weather patterns (Dimri
et al., 2013), vegetation and biodiversity patterns (Anderson
and Ferree, 2010), and hydrological processes (Iordanishvili,
2000), while fine-scale features such as local slope may influ-
ence soil stability (Buscarnera and Di Prisco, 2013) or influ-
ence nest-site selection by certain bird species (Whittingham
et al., 2002). Overall, topography is known to influence gra-
dients in moisture, energy, and nutrients across the landscape
(Hengl and MacMillan, 2009). Likewise, the oceans play
a fundamental role in the Earth system at multiple scales.
Knowledge of seafloor topography is also crucial for many
subjects (Smith, 2004). For example, seafloor topography, or
bathymetry, influences surface currents (Gille et al., 2004),
near-bottom currents (White et al., 2007), and ocean mixing
rates (Kunze and Llewellyn Smith, 2004). Lack of knowl-
edge on factors influenced by bathymetry can affect the ef-
ficacy of model predictions, for example models of marine
species distributions (McArthur et al., 2010), climate (Jayne
et al., 2004), or the paths of floating objects like marine de-
bris (Smith and Marks, 2014).
It is commonly stated that 90 % of the global ocean is un-
explored (e.g. Gjerde, 2006) and that more is known about
the surface of Earth’s Moon, Mars, Mercury, or Venus than
about the ocean floor (Sandwell et al., 2002; Smith and
Marks, 2014). However, such statements mean little without
further specification or elaboration on their real meaning in
relation to objectives, data types and spatial resolution. The
entire ocean floor has been mapped to a resolution of a few
kilometres using satellites, which has created an estimated
surface of global bathymetry (Smith and Sandwell, 1994).
However, these coarse-resolution data are often inadequate
for many scientific, economic, public safety, and manage-
ment purposes. Applications such as tsunami hazard assess-
ment, submarine cable and pipeline route planning, resource
exploration, habitat mapping, territorial claims, navigation,
and ocean circulation and climate studies all require more
reliable, fine-scale bathymetric data (i.e. finer than 5 km)
(Sandwell et al., 2002).
Fuelled by advancements in remote sensing and geo-
graphic information systems (GISs) (e.g. Grohmann, 2004),
the field of geomorphometry has entered a new era in re-
cent decades (Evans and Minár, 2011; Florinsky, 2012). Ge-
omorphometry is defined as the science on which quanti-
tative measurements of terrain morphology are based, with
foundations in geosciences, mathematics, and computer sci-
ences (Chorley et al., 1957; Mark, 1975; Pike et al., 2009).
It can be divided into two sub-fields: general geomorphome-
try (e.g. Minár et al., 2013), and specific geomorphometry
(e.g. Dr˘
agu¸t and Blaschke, 2006). General geomorphome-
try deals with continuous surfaces in order to extract terrain
attributes (e.g. slope, aspect, rugosity), while specific geo-
morphometry aims at characterizing or extracting discrete
landforms (Evans, 1972). The science of geomorphometry,
including its theories, methods, algorithms, and tools, was
mainly developed and tested on artificial (e.g. Jones, 1998;
Qin et al., 2013), terrestrial (e.g. Grohmann, 2015; Rigol-
Sanchez et al., 2015), and extra-terrestrial settings (e.g. Li
et al., 2014; Podobnikar and Székely, 2015). These methods
are relevant for underwater applications and have been in-
creasingly used in the last decade (Lecours et al., 2015a), but
differences in the nature of the input data (e.g. no need to
hydrologically correct the surface model, little to no ability
to validate measurements on the terrain) can sometimes pro-
duce different results than expected from land-based studies,
creating the additional need for a dedicated scientific effort
in marine geomorphometry. To our knowledge, no review on
the state-of-the-art of marine geomorphometry has ever been
This contribution aims to raise awareness of the rela-
tively recent field of marine geomorphometry by providing
an overview of current practices and application areas and
summarising the relevant literature to date. We first discuss
the gradual rise in the application and development of marine
geomorphometry (Sect. 1.2), from the first marine geophys-
ical applications to the latest developments in marine habi-
tat mapping and geomorphology. The paper then addresses
the five main steps of geomorphometry identified by Pike
et al. (2009) and adopted by the community (Bishop et al.,
2012), with a focus on how these steps are relevant to ma-
rine geomorphometry and different from traditional, terres-
trial geomorphometry (Fig. 1). Section 2 reviews the first
step of the geomorphometry workflow, which is to sample
the surface. The characteristics of bathymetric data collected
from four types of remote sensing techniques are described:
satellite radar altimetry, optical remote sensing, acoustic re-
mote sensing, and lidar (light detection and ranging). Sec-
tion 3, addressing the step in which we need to generate a
surface model from the sampled heights, discusses elements
that have implications on how the seafloor is represented
as data, including the interpolation methods used to create
models and the spatial scale (i.e. spatial resolution and ex-
tent) at which to generate them. Section 4 addresses the pre-
processing step that corresponds to making the surface model
ready for the next step, surface analysis, which is reviewed in
Sect. 5. The pre-processing of the surface model involves the
correction for errors, artefacts, and erroneous data in the sur-
face model. The analysis of the surface is the core of the ge-
omorphometric workflow and consists in deriving terrain at-
tributes and terrain features (or objects). Finally, the last step
of this workflow is the use of the derived terrain attributes
or features for a particular problem or application. The main
disciplines in which marine geomorphometry has been ap-
plied and developed are examined in Sect. 6, and we also
suggest other fields of research and applications that could
benefit from the integration of marine geomorphometry in
their practices. We conclude this review with recommenda-
Hydrol. Earth Syst. Sci., 20, 3207–3244, 2016
V. Lecours et al.: A review of marine geomorphometry 3209
Relevant reviews/discussions Sections
2.1 Satellite radar altimetry Calmant and Beaudry (1996), Sandwell and Smith (2001)
2.2 Optical remote sensing Klemas (2011a, 2013), Jawak et al. (2015)
2.3 Acoustic remote sensing De Moustier (1988), Dowling and Sabra (2015)
2.4 Bathymetric lidar Guenther et al. (2002), Klemas (2011b), Jawak et al. (2015)
Sampling the depth
of the seafloor
3. Interpolation, spatial scale Calder (2003), Tang et al. (2006), Hengl and Evans (2009),
Li and Heap (2011), Zhang et al. (2014)
Generating a digital
bathymetric model
5.1 General geomorphometry Evans (1972, 2003), Bishop et al. (2012)
5.2 Specific geomorphometry Jarvis and Clifford (2003), Minár and Evans (2008),
Drăguţ and Blaschke (2006), Drăguţ and Eisank (2011)
Analysing the digital
bathymetric model
6.1 Marine geomorphology Micallef et al. (2008), Harris et al. (2014)
6.2 Marine habitat mapping Wilson et al. (2007), McArthur et al. (2010), Dolan (2012)
6.3 Hydrodynamics Sandwell et al. (2002), Gille et al. (2004), Normile (2014)
6.4 Other applications Wood (2003), Passaro et al. (2013), Schimel et al. (2015)
4. Correcting errors/artefacts Debese (2001), Hughes-Clarke (1998, 2003a),
Oksanen and Sarjakoski (2005), Fisher and Tate (2006),
Reuter et al. (2009), Temme et al. (2009)
Figure 1. Geomorphometry is commonly implemented in five steps (Pike et al., 2009; Bishop et al., 2012), here adapted to the application
of geomorphometry to the marine environment (left). The panels on the right describe the structure and elements addressed in Sects. 2 to 6
of this paper, and list reviews and important discussion papers on these elements.
tions and reflections on the future of marine geomorphome-
1.2 The rise of marine geomorphometry
The science of geomorphometry has roots in morphography,
hypsometry, cartometric, geophysics, and geomorphology
(Pike et al., 2009; Evans and Minár, 2011; Evans, 2013). Fol-
lowing the increase in digital terrain models (DTMs) avail-
ability in the 1960s, the underlying theories and mathemat-
ical developments of modern geomorphometry (i.e. based
on quantitative measurements rather than qualitative obser-
vations derived from DTMs) started to be developed in the
early 1970s (e.g. Carson and Kirby, 1972; Evans, 1972; Kr-
cho, 1973; Schabber et al., 1979). These methods and algo-
rithms were gradually automated in the 1980s as computers
became more available (e.g. Horn, 1981; Imhof, 1982; Pike,
1988). However, constraints in computing power (Burrough,
1986) delayed the rapid expansion of geomorphometry until
the early 1990s (Pike et al., 2009). As mentioned in recent
reviews (e.g. Gessler et al., 2009; Evans and Minár, 2011,
p. 105), the field of modern geomorphometry is a “young
field” that is “still forming, with many concepts, methods and
In the marine environment, early geophysical research
that studied the link between the shape of the seafloor
and elements such as global tectonics (Parsons and Scal-
ter, 1977; Wessel and Chandler, 2011; references therein)
led the way to the more recent marine applications of mod-
ern geomorphometry. The first applications of quantitative
measurements derived from marine DTMs came from the
field of marine geomorphology (e.g. Czarnecki and Bergin,
1986; Shaw and Smith, 1987, 1990; Malinverno, 1990; Goff,
1992, 2001), and was sometimes referred to as mathemati-
cal morphology or geology, or simply seafloor classification
(Herzfeld, 1993). Then, the realization that different charac-
teristics of seabed morphology was often linked to species
distribution and biodiversity (e.g. Burrows et al., 2003; Gi-
annoulaki et al., 2006), combined with the increased avail-
ability of higher-resolution bathymetric data (Smith and Mc-
Connaughey, 2016), opened a wide range of possibilities for
marine habitat mapping in the mid-2000s (Bakran-Petriocili
et al., 2006; Lundblad et al., 2006; Wilson et al., 2007). As
discussed in Sect. 6, these two applications are still leading
research areas in marine geomorphometry, as new applica-
tions slowly emerge. While qualitative descriptions of terrain
morphology from DTMs are common in the literature (e.g. in
geophysics), the current review focuses on modern geomor-
phometry, i.e. the extraction of quantitative information from
depth models to describe terrain characteristics.
Figure 2 compares the increase in publications in both ma-
rine and terrestrial (and potentially extra-terrestrial) geomor-
phometry over time. The numbers illustrate that marine ap-
plications of geomorphometry are more recent and less nu-
merous than their terrestrial counterparts. However, we note
that the lower number of published marine applications indi-
cated in Fig. 2 may be biased by the fact that the researched
terms (e.g. geomorphometry and terrain analysis) are not al-
ways used in marine studies, even where geomorphometric
techniques have been employed. Consequently, Fig. 2 may be
a reflection of how these terms have been adopted rather than
a representation of the actual evolution of the practice. For in-
stance, in Harris and Baker (2012a), an authoritative volume
in marine habitat mapping, all of the 57 case studies used Hydrol. Earth Syst. Sci., 20, 3207–3244, 2016
3210 V. Lecours et al.: A review of marine geomorphometry
Figure 2. Cumulative number of publications (articles or reviews) listed in the Scopus database mentioning specific keywords in their title,
abstract of keywords, by the end of June 2016 for land-based (top) and marine (bottom) geomorphometry publications. For the “geomor-
phometry” curves, the keywords “geomorphometry” and “geomorphometric” were queried. The keyword “terrain analysis” was researched
for the “terrain analysis” curves. For the terrestrial applications, the following terms were used to query the database: “topographic variables”,
“topographic attributes”, “topographic derivatives”, “terrain variables”, “terrain attributes”, “terrain derivatives”, and “terrain morphology”.
These terms were also used for marine applications, in addition to “bathymetric variables”, “bathymetric attributes”, “bathymetric deriva-
tives”, “seafloor morphology”, and “seabed morphology”. The queries were all performed to exclude (for land-based publications) or include
(for marine publications) the terms “marine”, “ocean”, and “underwater”. We note that some common terms in the field (e.g. surface pa-
rameters, seafloor characterization) were not included because of their parallel use and different meaning in other fields that do not involve
bathymetry, 33 of them generated slope, 23 of them mea-
sured rugosity, and 14 of them calculated a topographic posi-
tion index, amongst other terrain attributes (Harris and Baker,
2012b). Despite this high use of general geomorphometry
techniques, the term “geomorphometry” was not once used
in the 900 pages of the volume, and “terrain analysis” was
only mentioned twice.
2 Sampling the depth of the seafloor
For centuries, the lead line was the main instrument used
to determine the depth of the seafloor, until remote sens-
ing technologies revolutionized the way we could measure
bathymetry. This section introduces four types of remote
sensing technology that are currently used to collect depth in-
formation and found in the geomorphometry literature: satel-
lite radar altimetry, optical remote sensing, acoustic remote
sensing, and bathymetric lidar. From an application perspec-
tive, the survey methodology or methodologies dictate the
spatial scale (i.e. resolution and extent) of the final surface
model. First, the fundamental technical limitations of the re-
mote sensing technique that is used to collect bathymetric
data will define the scale (resolution) of the surface model
(Kenny et al., 2003; Van Rein et al., 2009). For instance,
radar altimetry data limit models constructed with them to
coarse, usually kilometre-scale resolution, while other meth-
ods can achieve up to centimetre-scale resolution models.
Hydrol. Earth Syst. Sci., 20, 3207–3244, 2016
V. Lecours et al.: A review of marine geomorphometry 3211
Second, by defining the distance between the platform and
the target, the characteristics of the former may also influence
the scale (extent) of the final model; a remotely sensed image
collected from a satellite will usually have a coarser resolu-
tion and cover a larger area than an image collected from an
aircraft or an unmanned aerial vehicle. While radar altime-
try and acoustic remote sensing are limited to deeper waters
and optical remotely sensed and bathymetric lidar data are
limited to shallower waters, there is some degree of over-
lap between the depths in which the various methods can
be applied. In terms of effort, systematic bathymetric sur-
vey could be performed with satellite-based methods within a
few years at a global scale (Sandwell et al., 2002), compared
to the estimated 600 years (Carron et al., 2001) that it would
take using acoustic remote sensing technologies. The differ-
ent techniques are discussed in the perspective of using the
information they collect to generate DTMs and perform ge-
omorphometric analyses. DTMs using bathymetric data are
hereafter referred to as digital bathymetric models (DBMs)
to distinguish them from digital elevation models (DEMs),
a term usually reserved for terrestrial elevation data. Other
techniques can be used to measure depth but are less com-
mon in the literature (e.g. ground-penetrating radar, Feurer
et al., 2008). More details on the underlying theories of these
four techniques can be found in the Appendix.
2.1 Satellite radar altimetry
In the 1970s, satellite-based radar altimeters were developed
as a method to study the oceans on a global scale (Dou-
glas et al., 1987; see the Appendix), which was a significant
improvement over the extent covered by very narrow ship
tracks. Consequently, the applications of altimetry-derived
bathymetric data are limited to the study of broadscale pat-
terns, processes, and features as they only provide low-
resolution estimates of bathymetry (Goff et al., 2004). Tech-
nological constraints and satellite orbits also prevent data
collection close to the poles and the coastline (Sandwell et
al., 2002). Some authors identified weaknesses in the method
and warned that predicted depths from altimetry may not
be reliable and should not be used for geodynamics stud-
ies (Smith, 1993), navigation, or hazard identification (Smith
and Sandwell, 1994). The main advantages of altimetry-
derived bathymetry are speed of collection and uniformity
of coverage (Mackenzie, 1997).
Two main altimetry-derived data sets are currently used
in applications of marine geomorphometry: the General
Bathymetric Chart of the Oceans (GEBCO, 2014) and
the Shuttle Radar Topography Mapping 30-arcsec database
(SRTM30_PLUS, Becker et al., 2009). They are both free
data sets that combine together elevation and bathymetric
data. The bathymetric parts were created by filling the gaps
between publicly available data sets from different sources
with radar altimetry (Smith and Sandwell, 1994; Becker
et al., 2009). These data sets have been used for instance
in habitat mapping and predictive modelling (e.g. Davies
et al., 2008; Knudby et al., 2013), conservation (e.g. Ross
and Howell, 2013), search and rescue operations (Smith and
Marks, 2014), and geomorphology (e.g. Harris et al., 2014).
Many works have found these data sets to be too coarse for
their purposes (e.g. Davies et al., 2008; McNutt, 2014). For
instance, Vierod et al. (2014) stated: “At present, the avail-
ability of bathymetric data at a resolution sufficient to inform
reliable terrain attribute predictors is a major limitation to the
ability of deep-sea species distribution models to make accu-
rate predictions of the distributions of benthic organisms.
For many applications, quality can also be just as important
as resolution (cf. Sects. 4 and 6).
2.2 Optical remote sensing
Of the four remote sensing methods presented in this section,
optical remote sensing is the least common in the marine ge-
omorphometry literature. However, it presents a cheaper al-
ternative to lidar data for collecting depth information in very
shallow coastal areas (Su et al., 2014), as satellites can cover
large areas in less time (Lafon et al., 2002; Wang and Philpot,
2007). Two main optical remote sensing groups of methods
are usually used to estimate bathymetry from optical remote
sensing: one based on the interactions of electromagnetic ra-
diations with water and one based on stereoscopy (see the
Methods based on electromagnetic radiations are limited
to shallow waters because of light attenuation within the wa-
ter column (Jawak et al., 2015). In theory, based on light
penetration in coastal waters, depths down to 50m could
be retrieved (Speight and Henderson, 2010). However, the
practical limit varies with local sea conditions. A maximum
of 30 m deep is usually achieved when local conditions are
exceptionally good (Collet et al., 2000; Jawak et al., 2015),
and most often a depth of 15 m is reported as being the per-
formance limit of optical remote sensing for bathymetry re-
trieval (e.g. Stumpf et al., 2003). Optical methods are sensi-
tive to errors caused by waves, turbidity, sunglint from spec-
ular reflection, heterogeneous and complex seafloors, and
the presence of shadow that artificially increases depth esti-
mates (Lafon et al., 2002; Louchard et al., 2003; Holman and
Haller, 2013; Eugenio et al., 2015). Some of these elements
can be corrected or accounted for. For instance, Knudby et
al. (2010) applied “deglinting” (Hedley et al., 2005; Kay et
al., 2009) and water column corrections (Lyzenga, 1978), in
addition to the common geometric and atmospheric correc-
tions, to IKONOS satellite images to create a 4m resolution
DBM from which was derived measures of seafloor rugosity.
The authors indicated, however, that noise prevented the use
of bathymetry at depths deeper than 15 m as rugosity values
were artificially increased.
For stereoscopy-based methods, photogrammetry applied
to pairs of stereo images can be used to build DBMs in a sim-
ilar way as it is done on land. Although possible (e.g. Stojic et Hydrol. Earth Syst. Sci., 20, 3207–3244, 2016
3212 V. Lecours et al.: A review of marine geomorphometry
al., 1998), through-water photogrammetry is challenging due
to the need to correct for the air–water interface (Feurer et al.,
2008). However, underwater photogrammetry (i.e. active re-
mote sensing) has been successfully applied at a fine scale to
reconstruct the digital terrain (e.g. Johnson-Roberson et al.,
2010; Kwasnitschka et al., 2013). The work by Friedman et
al. (2012) is noteworthy as they derived multi-scale measures
of rugosity, slope, and aspect from underwater stereo image
2.3 Acoustic remote sensing
The development of acoustic technologies has fuelled ma-
rine exploration probably more than any other method, by
providing reliable swath coverage and relatively high-density
data at ever-decreasing price per line kilometre (see Lur-
ton, 2010 for review). Three types of active sonars (Sound
Navigation and Ranging) can be used to collect depth esti-
mates and/or backscatter information using sound waves (see
the Appendix): side-scan sonars (SSSs; reviewed in Blondel
and Murton, 1997), single-beam echo sounders (SBESs), and
multibeam echo sounders (MBESs; reviewed in de Moustier,
1988). Backscatter data are effective in providing infor-
mation on seafloor properties (e.g. sediment composition).
These tools can be pole-mounted on the side of vessels, or
mounted on the hull of vessels, on remotely operated vehi-
cles (ROVs), on autonomous underwater vehicles (AUVs),
or on a towed platform.
SSSs provide an acoustic image of the seafloor from
backscatter measurements that can inform on topographic
roughness. They can only provide bathymetric measurements
when data from two receiving antennas are combined and
principles from interferometry are applied. SSSs are a com-
monly used seafloor technology because they are easy to de-
ploy and cheaper than other acoustic technologies (Harris
and Baker, 2012c). The acoustic image quality of SSS im-
ages are very high resolution, and characteristics (e.g. length
and shape) of the acoustic shadows, which are the areas on a
SSS image that have null intensity values because the sound
was blocked by an object of feature higher than its surround-
ings, enable the estimation of the height and size of these
objects or features (Blondel and Murton, 1997). Collier and
Humber (2012) provided an example of the use of side-scan-
derived bathymetry to identify geomorphic features on the
seafloor. Some techniques from specific geomorphometry are
used on backscatter data to identify specific bedforms or de-
positional units on the seafloor based on their unique acous-
tic signature (Greene et al., 1999; Huvenne et al., 2005; Mar-
torelli et al., 2012) and to detect differences in reflectivity and
texture patterns on the seafloor (van Lancker et al., 2012).
We also recognize the potential to generate higher-resolution
(centimetre-scale) bathymetric data using modern synthetic
aperture sonar systems such as the HISAS 1030 (Kongsberg
Maritime, 2015) from a stable AUV platform (e.g. Ludvigsen
et al., 2014). There are many potential benefits to this ap-
proach although, at present, the processing of bathymetric
data is very computationally demanding and therefore best
suited to mapping of small areas.
SBESs, or fathometers, collect both depth and backscat-
ter data by transmitting a single sound beam at nadir. The
mapped extent is thus limited to a single track directly be-
low the supporting platform. Although they remain stan-
dard for ships navigation, SBESs are less and less used for
mapping purposes since MBESs became more affordable.
However, recent applications can still be found, particularly
where compiled SBES data are available; a SBES bathymet-
ric data set of the English Channel was used by Coggan and
Diesing (2012) for the broadscale analysis of an exposed
rock ridge system, by Elvenes et al. (2014) for surficial sed-
iment and habitat mapping, and by James et al. (2012) to
identify geomorphic features in a palaeo-valley.
MBESs provide a relatively fast, high-resolution, and wide
coverage measurement of the seafloor. They sweep a large
swath of the seafloor by emitting a fan of narrow sound
beams, and are currently the most efficient and accurate tool
available to collect bathymetric and backscatter data (Costa
et al., 2009; Schimel et al., 2010a, b). In recent years, ad-
vancement in MBES technology has further enhanced a valu-
able source of seafloor data. These advances have come out
of traditional user groups extending the application of the
data to meet new requirements and from the motivation of
new user groups wanting to employ the technology. This
wide-ranging and ever growing community of MBES users
are adapting and extending the potential of MBES data to
address unique applications. MBES users have traditionally
included hydrographers, navigators, engineers, marine ge-
ologists, and military planners, but now we see the exten-
sion of the technology to meet the needs of maritime explor-
ers, archaeologists, fisheries biologists, geomorphologists,
and ecosystem modellers, to name a few. MBESs are cur-
rently the main source of bathymetric data for applications of
marine geomorphometry, although these data are limited in
terms of coverage: “Multibeam soundings are the gold stan-
dards, but such mapping has been concentrated in coastal
zones, along shipping lanes, and in regions harbouring hy-
drocarbon or mineral deposits” (Normile, 2014, p. 964).
2.4 Bathymetric lidar
Bathymetric lidar is an adaptation of the more traditional air-
borne topographic lidar (Guenther et al., 2002; see the Ap-
pendix) and has become increasingly common in the litera-
ture in the last 2 decades (Brock and Purkis, 2009). Recently,
they have been combined into topo-bathymetric lidar, which
are multispectral systems that enable data collection both
above land and water; when flying over the water, a green
laser – characteristic of bathymetric lidar – penetrates the sea
surface and collects information on the water column and the
seafloor, while the red/infrared laser – characteristic of topo-
graphic lidar – collects information on the sea surface. Lidar
Hydrol. Earth Syst. Sci., 20, 3207–3244, 2016
V. Lecours et al.: A review of marine geomorphometry 3213
can also collect intensity values that, like acoustic backscat-
ter, provide information on the characteristics of the seafloor
(Costa et al., 2009; Kashani et al., 2015).
Bathymetric lidar is the only technique that can collect
high-resolution data in very shallow waters, which makes
it especially relevant for coastal applications requiring fine-
scale data (< 1 m resolution) (Brock and Purkis, 2009). The
efficiency of bathymetric lidar systems is greatly limited by
turbidity, wave action, depth (up to 50–70 m in exceptionally
good conditions), steep slopes, and rocky substrate (Costa
et al., 2009; Chust et al., 2010; Jalali et al., 2015). Current
geomorphometric applications on bathymetric lidar data are
mainly related to the exploration of coastal ecosystems (e.g.
Wedding et al., 2008; Zavalas et al., 2014) and geomorphol-
ogy (e.g. Arifin and Kennedy, 2011; Kennedy et al., 2014),
but are likely to extend to other applications such as marine
archaeology and natural hazards assessment (e.g. Solsten and
Aitken, 2006). In 2015, lidar data represented 4.5 % of the
coastal data collected for the Continually Updated Shoreline
Product (CUSP) compiled by the National Oceanic and At-
mospheric Administration (NOAA) and the National Geode-
tic Survey (NGS) of the United States (Graham et al., 2015).
3 Generating a surface model from sampled depths
A detailed account of the various approaches to processing
(i.e. georeferencing and applying system and environment-
related corrections) and cleaning of data (i.e. removal of spu-
rious depth soundings) are beyond the scope of this paper and
are specific to the sensors used for data acquisition as well
as industry and application-related standard practices. In this
section, we focus on the interpolation of data and only touch
briefly on data cleaning when we present a method where un-
certainty algorithms are used to aid data cleaning, and where
the interpolation of data is intrinsically linked to the calcula-
tion of uncertainty of the bathymetric surface.
By nature, geomorphometric analyses necessitate spatially
continuous data, but no remote sensing techniques used to
collect depth samples create truly continuous surfaces. Hengl
and Evans (2009) identified several techniques used to gen-
erate gridded DTMs from height samples for geomorphome-
tric purposes, including inverse distance weighting (IDW),
minimum curvature, spline, kriging, polynomial regression,
moving average, and many others. The same methods can
all be used to generate DBMs from depth samples. For in-
stance, Ezhova et al. (2012) created a DBM from SBES data
using the natural neighbour interpolation method, and Ramil-
lien and Cazenave (1997) combined altimetry and ship-
based data into a single DBM using bilinear interpolation.
More rarely, triangulated irregular networks (TINs) are cre-
ated from depth samples; for example, Heyman and Ko-
bara (2012) generated a TIN from SBES data, and Foster et
al. (2009) computed TINs from SBES and bathymetric li-
dar from which volumetric attributes were computed. The
choice of interpolator varies depending on the type of data
and the spatial arrangement of the depth samples. For in-
stance, MBESs or lidar data can collect very dense point
clouds that require little interpolation between points, result-
ing in limited interpolator influence in the final DBM. On
the other hand, creating a DBM for a big area from SBES
data requires more interpolation as SBESs only sample very
narrow tracks and have a high density of points along the
survey line but no data between the survey lines. This has
implications for geomorphometry as the interpolated DBM
may miss important geomorphological features (depending
on the distance between the survey lines), similarly to what
happens with the interpolation of contour lines (Wise, 1998).
Also, some methods (e.g. IDW) do not extrapolate and are
hence less accurate in cases of sparse sampling. The choice
of an appropriate interpolator to generate a surface model is
critical as some interpolators may produce erroneous depth
values that do not adequately represent the real bathymetry
(Smith and Wessel, 1990).
There are no optimal interpolation methods (Li et al.,
2005), and it is well known that each technique has differ-
ent sensitivity to errors and sample distribution and that the
quality of DTMs can be improved when making the appro-
priate choice of interpolator (Carrara et al., 1997; Hengl and
Evans, 2009). For instance, some techniques will consider
all samples while others will ignore outliers or smooth out
their effect. By being different in nature, sampled depths may
not require the same characteristics from an interpolator than
sampled elevations; for instance, DBMs do not need to be hy-
drologically corrected as drainage analyses are futile under-
water. This is why techniques were developed in recent years
to address the particular characteristics of depth sampling.
Here we examine such techniques, the CUBE (Combined
Uncertainty and Bathymetric Estimator) algorithm (Calder,
2003), which accounts for different errors specific to acoustic
remote sensing (e.g. geometric and acoustic) and is incorpo-
rated in several of the most widely used bathymetric process-
ing software used by the hydrographic survey industry and
scientific community. Although not yet universally accepted
as data cleaning method by the hydrographic survey indus-
try and hydrographic agencies, who have a particular need
to preserve shoal soundings and comply with strict quality
control procedures to ensure safety of navigation, CUBE is
widely used and of special interest to more applied bathymet-
ric data users and the related scientific community. Accord-
ing to Schimel et al. (2010b), CUBE could be more appro-
priate than traditional gridding methods to compute precise
bathymetry and associated terrain attributes. CUBE is based
on the spatially explicit quantification of the total propagated
uncertainty (TPU) for each data point (Calder and Mayer,
2003), enabling the rejection of samples that are outside a
certain uncertainty confidence level (e.g. 95% for Calvert
et al., 2015). When creating the DBM, the algorithm pro-
vides vertical error estimates and statistically assigns, to each
pixel, the most likely depth value based on the uncertainty
of each sounding within the pixel (see Dolan and Lucieer, Hydrol. Earth Syst. Sci., 20, 3207–3244, 2016
3214 V. Lecours et al.: A review of marine geomorphometry
Figure 3. Example of elements that can be extracted and visualized when using the CUBE algorithm, using the ROV-based data set from
Fig. 4 (source: Lecours and Devillers, 2015). In the top panel, the components contributing to the horizontal and vertical TPUs can be studied.
Other marginal contributions to the vertical TPU included the roll and pitch of the platform, timing of the inertial measurement unit, and
uncertainty associated with the sonar system (range and angle). The combination of the GPS and delta draft provides the three-dimensional
position of the soundings (x,y,z); in ROV-based research, the positional accuracy decreases with depth (Lecours and Devillers, 2015). In
the bottom panel, it is possible to visualize how the uncertainty and the density of soundings vary spatially.
2014). In several bathymetric processing software offering
CUBE, users can visualize not only the most probable depth
for each pixel, but also the subsequent most probable depths
(e.g. second or third most likely) and select the one they
think is the most appropriate based on their knowledge of an
area. For example, this can allow correction for occurrences
when the sonar detects fish close to the seafloor instead of the
seafloor itself and data cleaning did not appropriately remove
these soundings. Figure 3 shows some of the information that
can be extracted and visualized from the application of the
CUBE algorithm. When interpolating the soundings to create
a DBM, the bathymetry, and the horizontal and vertical com-
ponents of uncertainty can be stored in a BASE (Bathymetry
Associated with Statistical Error) surface. The BASE format
allows multi-attributes surface models. CUBE’s main incon-
venience is that it requires a lot of ancillary data to be col-
lected in order to compute TPU, but it is very reliable in
defining the spatial pattern of errors, their importance, and
helping to identify their sources (Passalacqua et al., 2015).
In addition to the bathymetry, a map of uncertainty can be
computed, which can become very important when making
decisions using the bathymetry and for onward geomorpho-
metric analysis.
Spatial scale is an important component of the interpola-
tion of depth data to generate DBMs, and differences in sam-
pling characteristics have an impact on the spatial scale of the
resulting surface model. Unlike systems used in optical re-
mote sensing, radar altimetry, and bathymetric lidar, acoustic
systems do not sample the seafloor uniformly, which influ-
ences the spatial scale of the resulting DBM. The sampling
density of these systems is directly dependent on depth, or
more specifically on the sensor-to-seafloor distance (Lecours
and Devillers, 2015). For instance, as the distance between
a MBES and the seafloor increases, it takes longer for the
Hydrol. Earth Syst. Sci., 20, 3207–3244, 2016
V. Lecours et al.: A review of marine geomorphometry 3215
Figure 4. Examples of errors and artefacts found in different data sets and their impact on derived terrain attributes. The top panels represent
data from GEBCO (2014), which uses radar altimetry to fill in the gaps between higher-resolution, freely available bathymetric data. The
main artefacts that can be observed are caused by the interpolation method that was used to combine the different data sets. For instance, a
linear artefact following a southwest to northeast axis can be observed as a result of the combination of one SBES acoustic survey line with
radar altimetry data. Similarly, some “spots” can be seen in the middle of the panels (south to north direction). These artefacts, especially
apparent in the curvature, are caused by the merging of punctual lead line measurements with the radar altimetry data. Finally, a slight
gridding artefact can be observed in the curvature (i.e. thin vertical and horizontal linear features). The middle panels show ship-based
MBES data (Brown et al., 2012). The obvious artefacts follow the surveying pattern of the vessel, and are mainly caused by vessel motion
that was not compensated properly by the motion sensor. Finally, the bottom row of panels corresponds to ROV-based MBES collected from
20 m above the seafloor in the deep sea (Lecours et al., 2013). In this case, the artefacts are caused by a combination of heave and other
platform motions; the ancillary data collected to account for this motion are too uncertain at this depth to appropriately correct for the errors
(Lecours and Devillers, 2015). The “spots” observed in the bottom and top of the derived terrain attributes are spurious soundings that can
be removed in bathymetric software during post-processing of the data. Note differences in spatial resolutions (left axis) and cartographic
scales. Depth values of the top left panel range from 60 to 4275m deep, those of the middle left panel range from 20 to 105 m deep, and
those of the bottom left panel range from 2345 to 2425 m deep. Lighter blue is shallower.
sound to reach the seafloor, the system’s footprint and re-
lated beam widths increase, leading to a lower sampling den-
sity for a greater area sampled at a coarser resolution. Since
the seafloor is rarely perfectly flat and at a constant depth,
the sampling density is almost never uniform across survey
areas, which can make it challenging to determine the ap-
propriate spatial resolution of DBMs for interpolation. Ulti-
mately, the spatial scale of a DBM will be dictated by its in-
tended use (see Sect. 6), which then influences the choice of
the data collection method, typically following hydrographic
standards (IHO, 2008) that ensure the appropriate data are
acquired to ensure safety of navigation. Besides DBMs cre-
ated directly from one source of survey data, we are in-
creasingly seeing DBMs generated or pooled together from
several surveys and/or sensor technologies (e.g. EMODnet,
2015). These data sets can be a valuable resource but impose
additional challenges for DBM creation and geomorphomet-
ric analysis (e.g. Sect. 6.4.3).
4 Correcting errors and artefacts in digital
bathymetric models
In terrestrial applications, it is well known that all DEMs,
regardless of the techniques used to collect and generate
data, are influenced by uncertainty and errors (Fisher and
Tate, 2006; Gessler et al., 2009). This is also true for ma-
rine applications, but the properties and dynamic nature of
the ocean makes DBMs more prone to errors and artefacts
than DEMs (Hughes-Clarke et al., 1996). As illustrated in
Fig. 4, this has significant implications for marine geomor-
phometry, which shows as widely recognized in the terres-
trial literature (Florinsky, 1998; Zhou and Liu, 2004; Oksa- Hydrol. Earth Syst. Sci., 20, 3207–3244, 2016
3216 V. Lecours et al.: A review of marine geomorphometry
nen and Sarjakoski, 2005) that errors and artefacts in a DTM
propagate to and may be amplified in terrain attributes.
As with DEMs (Harrison et al., 2009; Sofia et al., 2013),
errors and artefacts in DBMs can be caused by the inter-
polation method (Erikstad et al., 2013), movement and po-
sitioning of the supporting platform (Hughes-Clarke et al.,
1996), and a temporal (Lecours and Devillers, 2015) or spa-
tial (Hughes-Clarke, 2003a, b) misalignment between the
different elements of the surveying system. Data from radar
altimetry are the least sensitive to platform motion (Smith
and Sandwell, 1994). However, large artefacts resulting from
fine-scale noise in the gravity field (Goff et al., 2004) and the
algorithms used to convert gravity data into bathymetric es-
timates (Dixon et al., 1983; Calmant and Beaudry, 1996) are
often characteristic of these data. Similar large linear arte-
facts can sometimes be found in satellite images (e.g. Kle-
mas, 2011a). The level of error in bathymetric data from
optical remote sensing is known to directly depend on wa-
ter depth as a result of light attenuation in the water column
(e.g. Liceaga-Correa and Euan-Avila, 2002). Recent studies
(Leon et al., 2013; Hamylton et al., 2015) have demonstrated
that the integration of the spatial structure of errors improves
bathymetric estimates derived from satellite images. Data
collected with acoustic methods are the most susceptible to
artefacts for several reasons. First, they are collected from
surface vessels/platforms or underwater vehicles that can be
strongly affected by environmental conditions such as waves
and wind. Furthermore, acoustic waves need to be corrected
for sound velocity. Without this correction the data will ex-
hibit artefacts broadly similar to those caused by an inap-
propriate correction of the atmospheric conditions in opti-
cal remote sensing (Li and Goldstein, 1990). Sound velocity
varies with temperature, salinity and pressure and the failure
to correct for these variations can induce refraction artefacts
in the DBM (Yang et al., 2007). This is particularly chal-
lenging as water column properties vary both spatially and
temporally, especially in the coastal zone where there is the
additional complication of freshwater input from rivers, and
are less predictable than atmospheric conditions (Cushman-
Roisin and Beckers, 2011). Tidal corrections are generally
applied using data from locally installed tide gauges, or mod-
elled tides, depending on the accuracy required. Finally, since
the surveying system is underwater, direct positioning using
the Global Positioning System (GPS) is not possible (Ro-
man and Singh, 2006). The level of error in the data is thus
strongly influenced by the accuracy of the different instru-
ments that provide ancillary data to estimate the position of
the system’s underwater components (Rattray et al., 2014;
Lecours and Devillers, 2015).
Figure 4 illustrates different types of errors and artefacts
that can be found in bathymetric data of different types and
at different scales, and their propagation to derived terrain at-
tributes. Artefacts commonly found in bathymetric data and
that often cannot be corrected using existing methods include
gridding and interpolation artefacts (e.g. in the top panels),
motion artefacts (e.g. middle and bottom panels), refraction
artefacts, and artefacts caused by the temporal or spatial un-
certainty associated with ancillary data (e.g. bottom panels).
Common errors include spurious soundings (e.g. bottom pan-
els). Artefacts in DBMs are difficult to handle properly as
depth generally cannot be ground-truthed, thus preventing
verification of whether or not a feature is natural or the re-
sult of an artefact (Li and Wu, 2006). Most marine environ-
ments are not easy to access and the collection of ground-
truth data is often limited by technological and logistical con-
straints (Solan et al., 2003; Robinson et al., 2011). Conse-
quently, ground-truthing of DBMs is not standard practice.
We note however that ground-truthing is often performed for
backscatter data to attempt matching sediment types with
acoustic reflectivity characteristics. As illustrated in Fig. 4,
artefacts in DBMs may be present at all scales and per-
sist, or are sometimes amplified in derived terrain attributes.
For instance, artefacts in the GEBCO data set are common
(Lecours et al., 2013; GEBCO, 2014; Fig. 4), arising mostly
from the merging of data sets of different quality. When the
artefacts are large they dominate the surface and cannot be
removed with traditional filtering methods (e.g. Gaussian fil-
tering) as this considerably affects the overall quality of the
surface (Passalacqua et al., 2015), and the artefacts are also
difficult to overcome when deriving terrain attributes even
by using multi-scale methods (Sect. 5.1). At a finer scale,
Yang et al. (2007) developed an algorithm to correct refrac-
tion artefacts, although this was only partially successful.
When the artefacts are smaller, it can be difficult to distin-
guish them from real fine-scale features such as sandwaves
or iceberg scourings (Hughes-Clarke et al., 1996), especially
when no underwater video data are available to confirm the
geomorphology of an area. This is particularly challenging
for marine geomorphometry as analyses are likely to capture
both the real features and the artefacts (Wilson et al., 2007).
Currently, the main ways to address artefacts in DBMs are to
apply filtering techniques, resample the data to coarser reso-
lutions, manually correct the data based on visual interpreta-
tion, and to use algorithms like CUBE that account for errors
and uncertainty. Most marine geomorphometry applications
simply disregard the presence of the remaining artefacts, ex-
cluding them for practical purposes by expert judgement.
5 Deriving terrain attributes and terrain features
Bathymetric data, particularly full coverage multibeam, or
lidar data, are well suited for the generation of quantitative
terrain attributes and terrain features. These attributes and
feature classifications can be very useful in describing, in-
terpreting, and classifying geomorphology in the marine en-
vironment, just as their terrestrial equivalents are on land.
These derived data sets can also be of further use in many
applications (cf. Sect. 6). With bathymetric data now avail-
able in many areas at comparable resolutions to terrestrial
DEMs, depending on the survey equipment used (cf. Sect. 2),
Hydrol. Earth Syst. Sci., 20, 3207–3244, 2016
V. Lecours et al.: A review of marine geomorphometry 3217
Figure 5. Illustration of the main types of terrain attributes that can be derived from bathymetry data. Modified after Wilson et al. (2007).
we can extract a similar level of information to that obtain-
able from terrestrial DEMs. Elsewhere, global (e.g. GEBCO,
2014) and regional (e.g. IBCAO (Jakobsson et al., 2012);
EMODnet, 2015) bathymetric data sets combining informa-
tion from many sources have become an impressive resource
and are being used routinely for many marine science appli-
cations, not least those including high seas areas which, as
yet, have little detailed coverage. This section reviews both
the use of general (i.e. terrain attributes) and specific (i.e. ter-
rain features) marine geomorphometry.
5.1 General geomorphometry (terrain attributes)
The calculation of terrain attributes (synonymous with ter-
rain/topographic variables) first requires some method for
mathematically representing the bathymetric surface. This
surface representation is then used to calculate the required
terrain attribute, and is typically achieved by either using
neighbourhood analysis of raster pixels, or by fitting a poly-
nomial expression to describe the surface. The computations
performed on DBMs, and the range of applications that these
derived terrain attributes are used for, are common to many
of those performed on DEMs for terrestrial applications. Dif-
ferences in analysis of bathymetric DBMs vs. DEMs are of-
ten more related to the meaning or application of the infor-
mation from the analysis. For instance, deriving a watershed
network underwater may be useful, e.g., for delineating po-
tential sediment pathways on the continental slope, but is a
deviation from the original intended purpose. A review of
terrain attributes was provided by Wilson et al. (2007) in the
context of marine benthic habitat mapping and updated by
Dolan et al. (2012). In addition, Brown et al. (2011) offer a
useful summary of the extent to which many of these vari-
ous terrain attributes have been employed within published
habitat mapping studies in the period 2000 to 2011. Habitat
mapping is currently one of the largest application areas for
these techniques. To our knowledge no equivalent reviews on
the use of general geomorphometry exist for marine geomor-
phology or other application areas.
Terrain variables can be grouped into four main types
describing different properties of the terrain – slope, ori-
entation, curvature/relative position, and terrain variability
(Fig. 5). It is beyond the scope of this paper to provide de-
tails on all the various options for computation; however, we
provide an overview of some of the most commonly used
terrain attributes in marine-based studies, as well as an in-
dication of some common calculation approaches (Table 1).
Here we note the geomorphological relevance and ecologi-
cal relevance of the various types of terrain attributes in the
context of seabed mapping. Whilst the effects on geomor-
phology are more direct, the popularity of terrain attributes
in benthic habitat mapping is, to a large extent, due to their
function as a surrogate (or proxy) in explaining the distribu-
tion of benthic fauna. In the absence of better, or alternate
information (e.g. gained from high-resolution oceanographic
data), proxy information such as whether a given location is
sheltered or exposed to dominant currents as indicated by its
position relative to neighbouring terrain, can be useful in de-
termining suitable habitat for a given species or community.
An elevated position for example may be advantageous for
suspension feeding organisms and act as a surrogate for the
direct need for food supply. Other terrain attributes may cap-
ture a proxy for shelter or other ecological advantage. This
topic is discussed further by Lecours et al. (2015b) including
the all-important effect of scale, which is linked both to data
resolution and the scale at which geomorphometric analysis
is conducted. Hydrol. Earth Syst. Sci., 20, 3207–3244, 2016
3218 V. Lecours et al.: A review of marine geomorphometry
Table 1. Summary of the most commonly used terrain attributes in marine-based studies, as well as an indication of some common calculation
approaches. Modified after Dolan et al. (2012). The term “multiple scale” refers to terrain attributes derived in turn using analysis windows
of different sizes. “Multiscale” refers to indices derived simultaneously over a range of window sizes. The more general term “multi-scale”
is used in this paper to refer to both types of analysis as well as geomorphometric analysis using data of different resolutions.
Slope Orientation Curvature Terrain variability
Stability of sediments
(ability to live in/on
local acceleration of
currents (food supply,
exposure, etc.)
Degree of exposure to
dominant and/or local
currents from a particu-
lar direction (food sup-
ply, sedimentation, lar-
val dispersion, etc.)
Index of exposure/
shelter, e.g., on a peak or
in a hollow (food
supply, sedimentation,
predators, etc.)
Index of degree of habitat
shelter from expo-
sure/predators (link to life
structural diversity
linked to biodiversity
Stability of sediments
(grain size);
local acceleration
of currents (erosion,
movement of sediments,
of bedforms)
Relation to direction
of dominant geomor-
phic processes
Flow, channelling
of sediments/currents,
hydrological, and
glacial processes;
useful in the classifica-
tion of landforms
Terrain variability and
structures present reflect
dominant geomorphic
Commonly com-
puted terrain at-
tribute and exam-
ple marine-based
Slope (Lundblad et al.,
2006; Lanier et al., 2007;
Micallef et al., 2012a;
Dolan and Lucieer, 2014)
Aspect (Galparsoro
et al., 2009), north-
ness/northerness and
(Monk et al., 2011)
Mean curvature (Dolan
et al., 2008);
profile curvature
(Guinan et al., 2009);
plan/planimetric curva-
ture (Ross et al., 2015);
Bathymetric Position
Index (BPI) (Monk et al.,
2010; Pirtle et al., 2015)
Rugosity (Dunn and
Halpin, 2009);
vector ruggedness
measure (VRM)
(Tempera et al., 2012);
relative relief
(Elvenes, 2013);
fractal dimension
(Wilson et al., 2007)
Commonly used
terrain attribute
and software
(algorithm refer-
Single scale:
slope: ArcGIS Spatial
Analyst (Horn, 1981)
Single scale:
aspect: ArcGIS Spatial
Analyst (Horn, 1981);
statistical aspect
BTM toolbox (Wright
et al., 2012)
Single scale:
mean, profile, and plan
curvature ArcGIS Spa-
tial Analyst (Zevenber-
gen and Thorne, 1987)
Single scale:
rugosity (surface
area/planar area ratio)
(Jenness, 2004)
Multiple scale slope:
r.param.scale, Landserf
(Wood, 1996);
Multiscale slope: Land-
serf (Wood, 1996)
Multiple scale aspect:
r.param.scale, Landserf
(Wood, 1996);
Multiscale aspect:
Landserf (Wood, 1996)
Several measures of
multiple scale curvature:
Landserf (Wood, 1996);
Multiple scale BPI
(Lundblad et al., 2006);
Multiscale curvature
Landserf (Wood, 1996)
Multiple scale VRM
(Sappington et al.,
Multiple scale relative re-
lief (Erikstad et al., 2013,
and references
Multiple and multiscale
fractal dimension – Land-
serf (Wood, 1996)
For GIS-based calculation of terrain attributes, extending
the analysis window beyond the basic 3×3 neighbourhood
is particularly useful in marine geomorphometry as it facil-
itates the identification of spatial scales that are relevant to
benthic communities (Lecours et al., 2015b) or to geomor-
phological interpretation (Shaw, 1992) and may also help to
overcome artefacts in the DBM (Wilson et al., 2007). The
multi-scale analysis methods developed by Wood (1996),
which built on the work of Evans (1972, 1980), have been
fundamental in establishing an appreciation of scale in ma-
rine and terrestrial geomorphometry alike. The associated
software package Landserf (Wood, 2009) puts multi-scale
Hydrol. Earth Syst. Sci., 20, 3207–3244, 2016
V. Lecours et al.: A review of marine geomorphometry 3219
analysis within easy reach of marine scientists and the use
of Landserf for DBM analysis took off following the early
applications of the software to bathymetric data (e.g. Wil-
son et al., 2007). Although Landserf 2.3 is still used by
many scientists requiring a stand-alone programme for geo-
morphometric analysis, Wood’s algorithms are now perhaps
more widely used among the marine community through the
GRASS module r.param.scale. The newly released ArcGe-
omorphometry toolbox (Rigol-Sanchez et al., 2015) offers
a means to access the Wood–Evans (and other) algorithms
for geomophometric analysis, and has the potential to pro-
vide a long-awaited, convenient multi-scale analysis option
for ArcGIS users.
One terrain attribute that is specifically tailored to analysis
of bathymetric data is the bathymetric position index (BPI)
(Lundblad et al., 2006), which is an adaptation of the topo-
graphic position index (TPI) by Weiss (2001) and a useful
measure of relative position that is simple to calculate over
different neighbourhood sizes. Although a relatively simple
algorithm to implement (Lunblad’s BPI indices can be per-
formed through the raster calculator (e.g. Wilson et al., 2007)
or scripting), many marine scientists make use of the Benthic
Terrain Modeler (BTM) Toolbox, which was first developed
following the study by Lundblad (2006). The current version
of BTM (Wright et al., 2012) for ArcGIS 10.1 and later has
seen around 4000 downloads in the period 2012–2015, a fig-
ure that gives a conservative estimate of how many scien-
tists are actually using the tool (S. Walbridge, ESRI, personal
communication, 2015). The BTM toolbox relies on ArcGIS
Spatial Analyst and includes tools for calculating slope, as-
pect, and terrain variability (rugosity, vector ruggedness mea-
sure – VRM) as well as methods for combining these into
geomorphic zones. It was launched for the scientific commu-
nity at a time when multibeam data were becoming widely
available and modern marine geomorphometry was becom-
ing established. The BTM toolbox quickly became popular
as a one-stop shop for terrain analysis and classification of
bathymetric data, offering a slightly more tailored solution,
and the ability to handle larger data sets than Landserf, with
at least the BPI index being computable at different scales
since the first release (now joined by VRM). The utility of
the BTM tool has been augmented in recent years through
updating of the terrain variability and aspect indices, and by
providing the tools as both an AddIn and as a stand-alone
ArcToolbox, providing greater flexibility to users who may
wish to benefit from all, or just part of, the functionality.
Several bathymetric data processing software (e.g. CARIS
HIPS and SIPS, QPS-Fledermaus) also have built in tools for
calculation of basic indices such as slope and rugosity, bring-
ing the functions directly to the bathymetric data user and re-
moving the need to search for and select from the vast array
of available methods. This has advantages of convenience for
some bathymetric data users, but in most applied projects the
computation of terrain attributes and further analysis will be
conducted in some generic GIS software. Although many of
the commercial software are currently limited to single-scale
analysis (3 ×3 rectangular neighbourhood) it has become
easier to find tools for multi-scale analysis, either directly in
open-source software (e.g. GRASS), through additional tool-
boxes (e.g. SEXTANTE for QGIS), or via scripting. Many
of these also give alternative choices for computation algo-
rithms, the effects of which are investigated by Dolan and
Lucieer (2014) using slope as an example.
Terrain variability has been a particularly popular ter-
rain attribute in relation to benthic habitat mapping. This
is largely due to the generally accepted link of terrain vari-
ability with biodiversity, which has, however, not yet been
fully established with regard to spatial scale (Lecours et al.,
2015b). Several measures of terrain variability have been
applied to DBMs (Table 1) with some proving suitable
for multi-scale analysis and others becoming problematic
at larger analysis scales (Wilson, 2006). A rugosity index,
which is the ratio of surface area to planar area (Jenness,
2004), remains perhaps the most widely applied method in
marine studies and this was implemented in early releases of
BTM. Both the VRM (Sappington et al. 2007) now incor-
porated in BTM, and the more recent Arc_Chord Rugosity
measure (Du Preez et al., 2014) offer alternatives that are
better decoupled from slope. Where slope and a terrain vari-
ability measures are to be used in further analysis, e.g., as
predictor variables for habitat modelling, it is particularly im-
portant that the user is aware of any autocorrelation or co-
variation between these attributes, so they can be handled
appropriately. Du Preez et al. (2014) listed several marine
studies among those who have ignored the need for decou-
pling. However, with methods like VRM and Arc-Chord ru-
gosity, or toolboxes like BTM and TASSE (Lecours, 2015)
now readily available, we trust that future studies will make
a conscious choice of the best geomorphometric analysis to
use for their particular application.
5.2 Specific geomorphometry (terrain features/objects)
Compared to general geomorphometry and the use of terrain
attributes, applications of computer-based specific geomor-
phometry are still relatively rare in the marine environment.
Calculation of terrain features generally relies on the com-
bined properties of several terrain attributes. For instance,
Lecours et al. (2013) used Troeh’s landform classification
(Shary et al., 2005), which uses different types of curvatures
to identify zones of relative deflection or accumulation and
transit zones, on bathymetric data. The authors also adapted
the landform classification by Weiss (2001), which combines
slope with TPI measures at different scales to identify up to
16 landform classes, for application within the marine envi-
ronment using BPI measures.
Terrain features such as crests and troughs can be extracted
through the use of pixel-based analysis (e.g. Blaszczyn-
ski, 1997; Wood and Dragicevic, 2007), but object-oriented
methods for landform classification have recently become in- Hydrol. Earth Syst. Sci., 20, 3207–3244, 2016
3220 V. Lecours et al.: A review of marine geomorphometry
creasingly popular and are beginning to make their mark on
marine studies (e.g. Lawrence et al., 2015) driven by an op-
portunity to analyse the DBM in conjunction with acoustic
backscatter data (an indicator of seabed sediment type) rather
than analysing the DBM alone, which offers several advan-
tages for seabed classification. Geographic Object-Based Im-
age Analysis (GeOBIA, OBIA) has been gaining some trac-
tion in the seabed mapping community as the spatial res-
olution of acoustic backscatter data improves (Diesing et
al., 2014). The basic processing units in object-based im-
age analysis are objects which are represented by textu-
ral changes in the acoustic backscatter image and are con-
strained by derived topographic variables (Benz et al., 2004).
GeOBIA allows for the quantitative extraction of image tex-
tures and features to be identified in the backscatter data and
the ability to relate these spatially to topographic variability
(Costa and Battista, 2013). Multi-resolution segmentation is
one of the most popular segmentation algorithms to delineate
homogeneous seabed segments (Lucieer, 2008; Lucieer and
Lamarche, 2011; Hasan, 2012; Eisank et al., 2014) and in the
terrestrial literature stands out as the most successful method
to delineate homogeneous terrain segments rather than land-
forms per se (e.g. Dr˘
agu¸t and Blaschke, 2006; Dr˘
agu¸t et
al., 2011; Blaschke et al., 2014). This has been successfully
demonstrated by Ismail et al. (2015) to identify and classify
submarine canyons. By combining both the spatial deriva-
tives of the DBM with GeOBIA variables, the authors were
able to perform an automated multiple-scale terrain analysis
to discriminate local and broadscale geomorphic features in
the marine landscape. This information was used not only
to delineate geomorphic seafloor features but also to iden-
tify properties that might influence biodiversity in a complex
terrain. Specific geomorphometry is currently not used to its
full potential in the marine environment.
6 Applications of marine geomorphometry
Pike et al. (2009) listed current and potential applications
of marine geomorphometry, including oceanography, coastal
geomorphology, geophysical analysis of global tectonics,
ocean currents, mineral exploration, fisheries managements,
navigation, and concealment of nuclear submarines. While
performing the meta-analysis that enabled the making of
Fig. 2, we were able to classify marine geomorphometry ar-
ticles into four main research areas: geomorphology, geo-
physics, and geohazards; habitat mapping, biogeography,
and ecology; hydrodynamics and modelling; and others. This
section thus introduces the most common applications of ge-
omorphometry in the marine environment, and discusses in
Sect. 6.4 the least common uses in addition to potential future
applications of marine geomorphometry. A selection of pub-
lished works that have utilized geomorphometric techniques
in their study of seafloor morphology is provided in Table 2.
6.1 Marine geomorphology, geophysics, and
Early geomorphometric studies of seafloor morphology in
the 1960s were limited by the one-dimensionality and the
low resolution of the bathymetric data that were available at
the time (e.g. Krause and Menard, 1965; Neidell, 1966). In
the last 3 decades, improvements in seafloor surveying tech-
nologies have resulted in a renewed interest in employing
geomorphometric techniques to study seafloor geomorphol-
ogy. Similar techniques have also been utilized in the inter-
pretation of side-scan sonar data (e.g. Blondel et al., 1998;
Carmichael et al., 1996; Huvenne et al., 2002; Mitchell and
Somers, 1989).
Geomorphometric techniques have generally performed
well in submarine environments. The use of specific geomor-
phometric techniques, where features of interest are identi-
fied prior to analysis, has involved examining how differ-
ent morphological parameters change spatially and with each
other. They have been amongst the most successful tech-
niques, particularly with regard to the study of submarine
mass movements, canyons and volcanoes. In the study of
submarine mass movements, the general approach has been
the prior identification of the boundaries of the landslides,
the measurements of a series of morphometric parameters
and their spatial and statistical analyses. This kind of ap-
proach has been applied to slope instability offshore Norway
(Haflidason et al., 2005; Issler et al., 2005; Micallef et al.,
2008), demonstrating the fractal characteristics of submarine
mass movement morphology and statistics, which has impor-
tant implications for submarine landslide modelling and haz-
ard assessment. It has also been employed on a finer scale
(Casalbore et al., 2011; Rovere et al., 2014) and a broader
scale (Hühnerbach et al., 2004; McAdoo et al., 2000; Moer-
naut and De Batist, 2011) to identify tsunamigenic landslides
and to provide interesting insights into failure frequency, pre-
conditioning factors, triggers and controls of submarine mass
movements in a wide range of environments, including lakes.
In submarine canyons, specific geomorphometric analyses of
submarine landslides has shown that landslides can be the
most efficient process removing material from canyons and
that their influence becomes more significant as the canyon
matures (Green and Uken, 2008; Micallef et al., 2012b).
Geomorphometric investigations of submarine canyon form
have generally focused on using morphological data to pro-
pose model of canyon erosion by turbidity currents (Mitchell,
2004, 2005; Vachtman et al., 2013). More recently, specific
geomorphometric techniques have been used to demonstrate
how canyons in passive, progradational margins develop into
geometrically self-similar systems that approach steady state
and higher drainage efficiency (Micallef et al., 2014b), and
how canyons in active margins fail to reach steady state be-
cause of continuous adjustment to perturbations associated
with tectonic displacements and base-level change (Micallef
et al., 2014a). The geomorphometric study of volcanoes has
Hydrol. Earth Syst. Sci., 20, 3207–3244, 2016
V. Lecours et al.: A review of marine geomorphometry 3221
Table 2. Selection of studies published in the last 3 decades that applied geomorphometric techniques to the marine environment. A particular
focus is given to marine geomorphology studies, as a few other documents (e.g. McArthur et al., 2010; Brown et al., 2011; Harris and Baker,
2012; Rengstorf et al., 2012; Lecours et al., 2015b) already summarized the extent to which many of these techniques have been employed
in habitat mapping studies, and many of these techniques have yet to be employed in other contexts.
Technique Reference Spatial domain Broad theme
General geomorphometry
Morphometric attributes
Basic geometrical analysis
Adams and Schlager (2000)
De Moustier and Matsumoto (1993)
Teide Group (1997)
Continental slope
Volcanic islands
Geomorphology and
Coggan and Diesing (2012)
Ezhova et al. (2012)
Continental shelf
Coastal and continental shelf
Habitat mapping and
Mofield et al. (2004) Continental slope and rise, abyssal hills Hydrodynamics
Passaro et al. (2013) Coastal Others
Morphometric attributes
and their statistical
Berkson and Matthews (1984)
Booth and O’Leary (1991)
Chakraborty et al. (2001)
Goff and Jordan (1988)
Kukowski et al. (2008)
Micallef et al. (2007a)
Mitchell et al. (2000)
Moskalik et al. (2014a)
Passaro et al. (2010)
Passaro et al. (2011)
Smith and Shaw (1989)
Continental slope and upper rise
Mid-ocean ridge, abyssal plain, seamounts
Continental slope
Continental slope
Mid-ocean ridge
Coastal and inner shelf
Seamount, volcanic island
Abyssal hills
Geomorphology and
Lucieer et al. (2013)
Hill et al. (2014)
Rengstorf et al. (2012)
Tong et al. (2013)
Micallef et al. (2012a)
Tempera et al. (2012)
Rengstorf et al. (2013)
Rengstorf et al. (2014)
Coastal to inner shelf
Coastal to inner shelf
Continental slope
Outer shelf
Coastal to inner shelf
Shelf to abyssal plain
Continental slope
Habitat mapping and
Mohn et al. (2014)
Tong et al. (2013)
Continental slope
Outer shelf
Solsten and Aitken (2006)
Stieglitz (2012)
Spectral analysis Fox and Hayes (1985)
Fox (1996)
Gilbert and Malinverno (1988)
Goff and Tucholke (1997)
Moskalik et al. (2014b)
Mid-ocean ridge
Mid-ocean ridge
Coastal and inner shelf
Geomorphology and
Geostatistical methods Herzfeld (1989)
Herzfeld and Higginson (1996)
Ismail et al. (2015)
Continental slope
Mid-ocean ridge
Continental slope
Geomorphology and
Diesing et al. (2014) Coastal to inner shelf Habitat mapping and
Hillman et al. (2015) Continental slope Hydrodynamics
quantitative representation
Harrison et al. (2011)
Micallef et al. (2007a)
Micallef et al. (2007b)
Mitchell and Clarke (1994)
Pratson and Ryan (1996)
Outer shelf
Continental slope
Continental slope
Continental shelf
Continental slope
Geomorphology and
Calvert et al. (2015) Coastal Habitat mapping and
Bøe et al. (2015) Continental slope Others Hydrol. Earth Syst. Sci., 20, 3207–3244, 2016
3222 V. Lecours et al.: A review of marine geomorphometry
Table 2. Continued.
Technique Reference Spatial domain Broad theme
Neural networks Jiang et al. (1993) Mid-ocean ridge Geomorphology and
Marsh and Brown (2009) Continental shelf Habitat mapping and
Other techniques Mountjoy et al. (2009)
Preston et al. (2001)
Continental slope
Coastal to inner shelf
Geomorphology and
Specific geomorphometry Casalbore et al. (2011)
Gee et al. (2001)
Green and Uken (2008)
Haflidason et al. (2005)
Hühnerbach et al. (2004)
Issler et al. (2005)
McAdoo et al. (2000)
Micallef et al. (2008)
Micallef and Mountjoy (2011)
Micallef et al. (2012b)
Micallef et al. (2014b)
Micallef et al. (2014a)
Mitchell and Searle (1998)
Mitchell et al. (2002)
Mitchell et al. (2003)
Mitchell (2003)
Mitchell (2004)
Mitchell (2005)
Rovere et al. (2014)
Roy et al. (2015)
Stretch et al. (2006)
Vachtman et al. (2013)
Volcanic island
Volcanic island
Continental slope
Continental slope
Continental slope and volcanic islands
Continental slope
Continental slope
Continental slope
Continental slope
Continental slope
Continental slope
Continental slope
Mid-ocean ridge
Volcanic island
Volcanic island
Volcanic island
Continental slope
Continental slope
Continental slope
Coastal to inner shelf
Volcanic island
Continental slope
Geomorphology and
Costa and Battista (2013)
Diesing et al. (2014)
Coastal to inner shelf
Habitat mapping and
been useful in determining the key processes constructing
and modifying volcano flanks and specifying the conditions
that lead to slope instability (Mitchell, 2003; Mitchell et al.,
2002; Stretch et al., 2006).
Initially, the techniques of general geomorphometry used
in the study of submarine landscapes were less numerous and
varied than those used in the study of subaerial landscapes.
The majority of studies where geomorphometry was applied
to the study of submarine landscapes have involved either
spectral analyses of the bathymetric data or the statistical
analysis of morphometric attributes (see Table 2 for exam-
ples). More recently, general geomorphometric studies have
made wider use of morphometric attributes and their statis-
tical analyses and feature-based quantitative representation,
most of which were specifically developed for submarine
landscapes. Micallef et al. (2007a), for example, developed
a methodology for the quantitative analysis of seafloor data,
which was shown to exploit the full potential of these data
sets and significantly improve the mapping and characteri-
zation of submarine landslides. This methodology was ap-
plied to the submarine mass movements offshore Norway to
elucidate the evolution dynamics of a multi-phase submarine
landslide (Fig. 7), while emphasising the potential role of gas
hydrate dissociation and contourite deposition in controlling
the location and extent of submarine slope failure (Micallef
et al., 2009), and to improve understanding of the mechanics
and triggers of spreading, also while using limit-equilibrium
and mechanical modelling (Micallef et al., 2007b). The au-
tomated and objective mapping of submarine landscapes is
indeed an important application of general geomorphometry,
and specific techniques have been developed for the char-
acterization of pockmarks (Harrison et al., 2011; Gafeira et
al., 2012), terraces (Passaro et al., 2011), and canyons (Is-
mail et al., 2015). Others have used general geomorphome-
tric techniques to classify submarine landscapes (e.g. fjords
(Moskalik et al., 2014a; Moskalik et al., 2014b), continen-
tal shelf and slope (Elvenes, 2013), and global (Harris et al.,
2014); identify the various styles and scales of deformation
across submarine landslides (Mountjoy et al., 2009); and in-
fer the evolution of seamounts (Passaro et al., 2010), mid-
ocean ridge scarps (Mitchell et al., 2000), and faults in active
continental margins (Kukowski et al., 2008).
Hydrol. Earth Syst. Sci., 20, 3207–3244, 2016
V. Lecours et al.: A review of marine geomorphometry 3223
Figure 6. Indicative workflow showing the use of terrain attributes
in habitat mapping. Generally following some pre-selection of vari-
ables the observed habitat points (response variable) are combined
with full coverage predictor variables selected from bathymetry, ter-
rain attributes, and other environmental variables as available to
form the input to a habitat model, which will be used to predict
a full-coverage habitat map. The choice of habitat model will de-
pend on the study in question but is typically either a statistical
(e.g. generalised linear models) or machine-learning-based models
(e.g. random forest). Observed habitat points are classified from vi-
sual or physical samples of the seabed. Terrain attributes are typi-
cally multi-scale and may include general and/or specific geomor-
phometry. Other environmental variables may include, for example,
oceanographic data (temperature, salinity, current speed, etc.) and
geological data (e.g. grain size). A similar workflow applies to mod-
elling of single species or communities, where the output will be a
continuous map indicating the probability of occurrence within the
study area, rather than a categorical map as shown here.
6.2 Marine habitat mapping, biogeography, and
Benthic habitat mapping is one of the major applications ar-
eas where the use of marine geomorphometry has grown in
recent years. Linked to the rise in the use of multibeam data
for benthic habitat mapping (Brown et al., 2011; Smith and
McConnaughey, 2016) the vast majority of habitat mapping
studies with access to good bathymetry data are now using,
or at least testing, some form of terrain attribute or feature
classification in their habitat mapping activities, even though
we note that many of these are not yet reflected in the peer-
reviewed literature. Among the habitat mapping community
several approaches to habitat mapping are common, many of
which directly incorporate biological data, such as modelling
species (e.g. Davies et al., 2008) or biotope distributions (e.g.
Elvenes et al., 2014) and others which are primarily based
on physical attributes deemed relevant for the distribution of
benthic fauna (e.g. Micallef et al., 2012a; Ismail et al., 2015).
Geomorphometry is equally useful for both these approaches
and those that combine both aspects (e.g. Tempera et al.,
2012) and this discussion is relevant to an all-encompassing
definition of habitat mapping (Lecours et al., 2015b). Fig-
ure 6 illustrates how terrain attributes are typically used in
the production of predictive seabed habitat maps, providing
an invaluable suite of full coverage predictor variables, which
are used together with point samples of observed habitat as
the input data to modelling.
Harris and Baker (2012b) provide a summary of surrogate
variables used for habitat mapping studies, including many
terrain attributes that have been applied across a multitude of
approaches to habitat mapping worldwide. The issue of sur-
rogacy is also discussed in this volume as well as by Lecours
et al. (2015b) and McArthur et al. (2010). The case studies
presented in the GeoHAB Atlas, and other published stud-
ies, vary in the degree to which they have established the
ecological relevance of the terrain attributes and/or feature
classifications used. For geomorphological variables to re-
ally be useful predictors of seafloor habitat, the relationship
between habitat and specific variables first needs to be estab-
lished. Apart from depth, which all of the geomorphologi-
cal variables are derived from, different shapes or attributes
of the seafloor will be relevant to different species at dif-
ferent scales over different bathymetric and biogeographic
zones. Bathymetry is known to have a first-order influence
on species distribution, largely because many properties that
directly affect benthic habitat vary with depth (e.g. light, tem-
perature). A number of recent papers describe the potential
of terrain attributes to act as surrogates of species distribu-
tion (Lucieer et al., 2013; Hill et al., 2014). The relationships
are validated using several different statistical methods that
either test terrain attributes against biological or ecological
data, or combine terrain attributes with other environmental
data and perform classifications to differentiate between the
different habitats (Thiers et al., 2014).
In an example by Rengstorf et al. (2013), habitat suitability
models for the cold-water coral Lophelia pertusa were devel-
oped based on full coverage multibeam bathymetry on the
Irish continental margin. Maximum entropy modelling was
used to predict L. pertusa reef distribution at a spatial reso-
lution of 0.002(250 m). Coral occurrences were assembled
from public databases, publications, and video footage, and
filtered for quality. Environmental predictor variables were
produced by re-sampling of global oceanographic data sets
and a regional ocean circulation model. Multi-scale terrain
parameters were computed from multibeam bathymetry at
50 m resolution. In a related study, Rengstorf et al. (2012)
examined the effect of bathymetric data resolution on terrain
attributes used to predict coral distribution, resampling the
original 50 m resolution bathymetry from the Irish National
Seabed Survey at successively coarser intervals up to 1 km.
They concluded that terrain attributes derived from higher-
resolution bathymetry are required to adequately detect the
topographic features relevant to corals. In a further related
study, Rengstorf et al. (2014) examined the relative impor-
tance of terrain attributes and hydrodynamic variables (e.g.
current speed, vertical flow, temperature) on models of cold-
water coral distribution, concluding that combining the envi-
ronmental information from these two sources leads to im-
proved predictions over the spatial scales in question. Hydrol. Earth Syst. Sci., 20, 3207–3244, 2016
3224 V. Lecours et al.: A review of marine geomorphometry
Figure 7. Example of the use of marine geomorphometry to semi-automatically map the components of mega-scale submarine landslide
offshore Norway (adapted from Micallef et al., 2009). (a–c) show the trough depth, ridge length, and ridge spacing extracted from a MBES
map of the north-eastern Storegga Slide using ridge characterization techniques (Micallef et al., 2007b). Figure d is a classification map
generated by using these ridge characteristic maps as input layers in an unsupervised clustering algorithm (ISODATA). (e) is an interpretative
map of the range of spreading events based on (a–d). Other mass movements and geological processes and structures have been interpreted
using geomorphometric mapping (Micallef et al., 2007a).
At a much finer-resolution (1 m) species–habitat rela-
tionships were examined across a marine reserve on the
south-eastern coast of Tasmania using boosted regression
tree analyses (Cameron et al., 2014). The most important
explanatory variables of community diversity were those
describing the degree of reef aspect deviation from east
and south (seemingly as a proxy for swell exposure), reef
bathymetry (depth), low rugosity, and slope. These models
could account for up to 30 % of the spatial variability in
measures of species diversity. As biological data at scales
relevant to acoustic or remote sensing data, such as that
from AUVs, ROVs, and diver surveys, become available on
national or international databases, such as the Census for
Marine Life and the Ocean Biogeographic Information Sys-
tem (OBIS), the ability to extend species distribution models
into the wider ocean at finer scales will enhance the utility
and value of marine geomorphometry variables for marine
biodiversity assessment.
6.3 Hydrodynamics and modelling
The interaction of bottom currents with seafloor sediments
results in a wide range of erosional and depositional mor-
phologies – e.g. scours, furrows, ripples, dunes, lineations,
contouritic drifts – the morphology and dimensions of which
depend on flow velocity and sediment grain size (Stow et al.,
2009). Detecting change in bedform morphology is of great
interest to geologists, physical oceanographers, and climatol-
ogists, and many others with the applied interest in such fea-
tures. Bedforms determine basic flow patterns of ocean cir-
culation at coarse and fine scales; even small perturbations in
seafloor topography can influence the pathway and velocity
of major shallow and deep current flows, heat transport, and
Hydrol. Earth Syst. Sci., 20, 3207–3244, 2016
V. Lecours et al.: A review of marine geomorphometry 3225
ultimately climate (Gille et al., 2004; Kunze and Llewellyn
Smith, 2004; Metzger and Hurlburt, 2001; Palomino et al.,
2011). In turn, bedforms are also excellent archives of cur-
rent and past bottom flow patterns (Sandwell et al., 2002).
Port managers are also interested in bedforms and their evo-
lution, particularly where they constitute a hazard to naviga-
tion in coastal waters. Detecting change in bathymetry and
its impact on oceanography is therefore important, and local
geomorphometric attributes, such as aspect, curvature, and
rugosity, have been used to develop hydrodynamic models
or as proxies for local and regional currents (Lecours et al.,
2015a). Seafloor topographic proxies are also fundamental in
the predictive mapping of suspension-feeders (Lucieer et al.,
2013; Hill et al., 2014), such as cold-water corals (Rengstorf
et al., 2012; Tong et al., 2013), because their distribution
is inextricably linked to current flow strengths and patterns
(Mohn et al., 2014). More recently, understanding the link
between seafloor morphometry and currents has been shown
to be essential in forecasting the path of floating debris from
tsunamis and air disasters and assist in search and rescue
operations (Mofield et al., 2004; Normile, 2014; Smith and
Marks, 2014).
6.4 Emerging and future applications
Other applications of marine geomorphometry can also be
found in the literature. We anticipate that the number of ap-
plication areas will grow substantially over the next few years
as awareness of data and analysis techniques expands, and
high-resolution data become more widely available.
6.4.1 Change detection
A number of studies have described temporal morphologi-
cal dynamics of the seafloor using acoustic bathymetry (e.g.
Duffy and Hughes-Clarke, 2005; Smith et al., 2007). How-
ever, assessments of biological change beyond the range of
optical sensors have been based primarily on ground sam-
pling methods. Rattray et al. (2013) investigated approaches
to quantify temporal change in benthic habitats from a spa-
tially explicit perspective using acoustic techniques. Their
methods (1) quantified change in terms of gains and losses
in the extent of habitat at a site on the temperate southeast
Australian continental shelf, (2) they could distinguish be-
tween systematic and random patterns of habitat change, and
(3) were able to assess the applicability of supervised acous-
tic remote sensing methods for broadscale habitat change as-
sessment. Change detection in temperate bedrock reefs were
identified through morphological characterization by Stor-
lazzi et al. (2013). They delineated the classes using a mul-
tivariate classification routine (Dartnell and Gardner, 2004)
based on acoustic backscatter and rugosity (surface-planar
area ratio).
There have also been several examples in the literature of
repeat multibeam surveys being used to detect change, many
of which are summarized by Schimel et al. (2015). Analy-
sis is generally focused on differences in depth values de-
tected and often aided by a visual assessment of the changes
in morphology. There are fewer studies that have explicitly
used terrain attributes or features in their assessments, but
we recognise the potential for gemorphometric techniques to
be more widely applied in this type of study. For example,
Bøe et al. (2015) used geomorphic feature detection (Wood,
1996) to identify crests and ridges in a sandwave field on
the continental slope, and assess movement between surveys
based on the change in position of these features. We note
also that Schimel et al. (2015) incorporate measures of bathy-
metric uncertainty in their assessment of volume change and
also recommend guidelines on thresholds, which can help to
improve the confidence of such assessments.
6.4.2 Seismic geomorphometry
Seismic geomorphology is a rapidly evolving discipline.
It comprises the application of geomorphological princi-
ples and analytical techniques to study palaeo-landscapes as
imaged by three-dimensional (3-D) seismic reflection data
(Carter, 2003; Posamentier and Kolla, 2003; Posamentier,
2003). More recently, 3-D seismic reflection data have also
provided a good alternative source of bathymetric data when
the latter are absent (e.g. broadscale geomorphic mapping
in the MAREANO project – The
development of seismic geomorphometry is a natural con-
sequence of increasing computer power, which enables the
rapid manipulation, visualization, and interpretation of 3-
D seismic reflection data, and the enormous investment in
this technology by the oil and gas industry, with academics
and government researchers benefitting from access to these
data. The integration of seismic geomorphology with seis-
mic stratigraphy currently represents the state-of-the-art ap-
proach to extracting geological information from 3-D seis-
mic reflection data to understand large-scale basin evolution.
Seismic geomorphological studies have addressed a broad
range of geological problems, ranging from sedimentary to
igneous geology, from lithology distribution to large-scale
tectonic analysis (e.g. Fachmi and Wood, 2003; Miall, 2003;
Wood, 2003).
Up to the present, most studies have focused on the qual-
itative recognition of broadscale features (e.g. Posamentier
et al., 1996; 2000; Peyton and Boettcher, 2000; Posamen-
tier, 2003; Zeng and Hentz, 2004). Quantitative seismic geo-
morphology, or seismic geomorphometry, is the most recent
development of seismic geomorphology (Carter, 2003; Posa-
mentier, 2003; Posamentier and Kolla, 2003). Seismic geo-
morphometry has been defined as the “quantitative analysis
of the landforms, imaged in 3-D seismic data, for the pur-
poses of understanding the history, processes and fill archi-
tecture of a basin” (Wood, 2003). Seismic geomorphometry
encompasses techniques that use 3-D seismic data to investi- Hydrol. Earth Syst. Sci., 20, 3207–3244, 2016
3226 V. Lecours et al.: A review of marine geomorphometry
gate the nature and architecture of reservoirs through extrac-
tion and analysis of quantitative morphometric information.
Great opportunities exist for applying a more quantitative
approach in seismic geomorphology. Seismic geomorpho-
metric techniques provide statistical and mathematical in-
sight into the morphological and dimensional characteristics
of geologic systems that are difficult to derive through qual-
itative investigations of outcrop exposures and 2-D seismic
reflection data. Seismic geomorphometric studies provide a
deep and spatially extensive understanding of how morphol-
ogy develops through time, providing insight into the histori-
cal evolution of a basin and the possibility of developing pre-
dictive models. Quantitative relationships derived from seis-
mic geomorphological studies can decrease our uncertainty
in predicting the nature and location of reservoirs in deep-
water settings by testing cause-and-effect relationships in a
variety of settings. Computer-assisted seismic geomorphom-
etry, in particular planform pattern recognition, is a powerful
addition to the seismic geomorphological approach. It allows
the interpreter to identify geologically significant features in
plan view automatically. The ability to exploit the full po-
tential of large seismic data sets is currently hindered by the
lack of tools in existing software packages, coupled with the
limited knowledge of how morphometrics can be used in the
analytical process. It is the development of such tools that
should be a main focus for researchers of marine geomor-
phometry in the near future.
6.4.3 Broadscale coastal geomorphometry
This paper has shown how geomorphometric techniques de-
veloped mostly in terrestrial settings can be applied to the
marine environment or adapted to enable quantification of
the seafloor terrain. For a long time, however, the boundary
between the land and the sea was not easily mapped or delin-
eated and represented a challenge for both marine and terres-
trial scientists (Klemas, 2011a). This was due to the inability
of satellite remote sensing to collect data in deep waters and
the limitations of acoustic systems to collect data in shallow
waters, which often creates a gap in terrain data where land
meets sea. This littoral gap, sometimes referred to as “the
white zone” because the lack of data in this area between
available DBM and DEM data appear white on maps, of-
ten complicates the study of nearshore environments and can
have important implications for applications such as naviga-
tion and geohazard assessment. For instance, in their attempt
to assess the effectiveness of a marine protected area in a
Canadian subarctic fjord with habitat maps generated from a
combination of terrain attributes and other data, Copeland et
al. (2013) were only able to map 32 km2of the total 82 km2
of the area. They highlighted the laborious nature of a shal-
low water survey (i.e. time- and cost-consuming MBES sur-
veys), the need for a continuous coverage because of the large
littoral gap, and indicated that interpolation and extrapola-
tion of results in the littoral gap were inappropriate because
of the heterogeneous nature of coastal fjord environments
(Copeland et al., 2013).
In the last 15 years, developments in lidar surveying meth-
ods (e.g. Hardin et al., 2014) and bathymetric lidar sys-
tems (cf. Sect. 2.4) slowly helped fill the littoral gap. Con-
sequently, efforts to map the littoral using bathymetric lidar
have spread across the globe (e.g. the National Coastal Map-
ping Program of the Joint Airborne Lidar Bathymetry Tech-
nical Center of Expertise in the United States), and several
examples of investigations of the coastal environment us-
ing geomorphometry can now be found in the literature (e.g.
Purkis et al., 2008; Pittman et al., 2009). This body of liter-
ature used to be mostly characterized by the study of small
areas either above the water (e.g. dunes or emergent features)
or submerged (e.g. coral reefs) (Brock and Purkis, 2009), but
there are now more and more efforts targeting the collection
of topo-bathymetric data that span the coastal environment
(e.g. Dunkin et al., 2011; Dunkin and McCormick, 2011).
Several fields can benefit from seamless coastal geomor-
phometric analysis. For instance, inter-tidal rocky shores are
known to shelter a lot of biodiversity (Kostylev et al., 2005)
and linking quantitative terrain attributes to measures of bio-
diversity could improve scientific understanding of ecolog-
ical patterns and processes in these important areas of the
land–sea boundary (e.g. Collin et al., 2012). So far, limita-
tions of lidar systems (e.g. inability to collect data in deeper
waters, costs associated with airborne surveys), however, re-
stricted these efforts to local and sometimes regional scales.
To our knowledge, there are no geomorphometric applica-
tions that span the terrestrial and underwater landscapes in a
continuous way over very large areas, which would require
the integration or fusion of data sets from different sources
(e.g. lidar, terrestrial DTMs, and acoustic surveys).
At a broader scale, a seamless analysis of terrestrial and
marine environments requires the combination of terrestrial
DTMs, bathymetric data from acoustic systems, and bathy-
metric lidar or optical remotely sensed data to fill the littoral
gap and create what has been called in the literature a coastal
terrain model (CTM) (Hogrefe et al., 2008; Leon et al.,
2013). The challenges encountered with merging data sets
from different sources makes such an approach still nascent
in the general literature (Macon et al., 2008; Quadros et al.,
2008; Collin et al., 2012), and very rare, if not fully absent,
in the marine geomorphometry literature. Data fusion is the
process of acquiring, processing, and synergistically combin-
ing multi-source data sets both geometrically (i.e. in space)
and topologically (i.e. in terms of their attributes or infor-
mation content) (Usery et al., 1995; Samadzadeghan, 2004;
Mohammadi et al., 2011). Despite constant developments
in data fusion (Pohl and van Genderen, 1998; Dong et al.,
2009; Zhang, 2010), it presents particular challenges for ge-
omorphometry. First, despite improvements in edge match-
ing algorithms, artefacts from merging and surveying can ap-
pear when deriving terrain attributes from the fused data set
(Stoker et al., 2009). Data fusion often requires the different
Hydrol. Earth Syst. Sci., 20, 3207–3244, 2016
V. Lecours et al.: A review of marine geomorphometry 3227
data sets to overlap slightly in order to be combined. In the-
ory, the overlapping areas should yield very similar values,
within their uncertainty and error ranges. However, impor-
tant inconsistencies (up to 6.5 m) have been reported between
depth measurements of the same areas using bathymetric li-
dar and MBESs (Quadros et al., 2008; Costa et al., 2009;
Chust et al., 2010; Shih et al., 2014). This has implications
for geomorphometry since terrain attributes will capture and
classify these mismatches as features, especially as the differ-
ences usually occur locally (Chust et al., 2010). Also, coastal
environments can be very dynamic; artefacts could appear
in the DTM if the multi-source data are not collected at the
same time and changes occurred between the data collec-
tions. Another issue concerns vertical datums (Hogrefe et
al., 2008); terrestrial surveys are usually referenced to a lo-
cal geoid model based on the GPS, while underwater acous-
tic surveys are usually referenced to the mean sea level at
the time of survey, which is referenced to a local or regional
tidal gauge that is itself referenced to a local datum. Calls for
a consistent and unified vertical datum have been made but
this issue is still unresolved (Hogrefe et al., 2008; Quadros
et al., 2008). Finally, data quality and uncertainty may com-
plicate the fusion of the different data sets. For instance, the
inability of bathymetric lidar systems to collect reliable data
in turbid or cloudy waters and in breaking wave conditions,
in addition to their difficulties to distinguish the seafloor from
the water surface in waters shallower than 30cm (Quadros et
al., 2008) may create a smaller littoral gap called the “dead
zone” (Nayegandhi et al., 2009) and prevent proper fusion.
Bernstein et al. (2011) recommend a customized survey de-
sign to minimize the challenges associated with creating a
seamless DTM.
Regular problems of data fusion, for instance related to
merging multi-resolution data sets or to software and format
compatibility/interoperability, also apply to the development
of DTMs for broader-scale coastal geomorphometry. Terres-
trial terrain models may have a digital elevation model (.dem)
format, bathymetric lidar data can be recorded with a laser
file (.las) format, and acoustic data can be saved in a Bathy-
metric Attributed Grid (.bag) format; all these file formats
have different structure and characteristics. Impediments to
the fusion of multisensor data to build seamless elevation
and depth surfaces include, but are not limited to, inconsis-
tent spatial and temporal scales, incompatible formats, and
differences in levels of reliability, uncertainty and complete-
ness. Despite these impediments, data fusion has been iden-
tified as a promising technique for geomorphometry (Bishop
et al., 2012).
Some authors (e.g. Quadros et al., 2008) argue that the dif-
ferent types of data sets cannot be readily integrated, but the
main challenges will likely be addressed with improvements
in data fusion techniques and ease of implementation of these
techniques for non-expert users (Zhang, 2010) for geomor-
phometry. Current work includes detection and correction of
differences in geoid models, consideration of uncertainties,
and improvement in edge matching algorithms (Quadros et
al., 2008; Dong et al., 2009; Stoker et al., 2009). Recently,
Leitão et al. (2016) proposed a new method to merge differ-
ent DTMs developed specifically for geomorphometry. Fu-
ture developments in data fusion will likely allow for bet-
ter integration of different data to create seamless coverage
for complete geomorphometric analysis and identification
of broadscale overlapping landforms between the different
realms. This will be useful for a wide range of coastal ap-
plications. For instance, observations of underwater and ter-
restrial landforms have shed light on how glaciers retreated
in Atlantic Canada during the last deglaciation (Shaw et
al., 2006); the investigation of landforms that overlap both
realms could help refine this type of analysis. Other poten-
tial applications include the investigation of coastal mor-
phodynamics and land–sea exchange modelling, dredging,
the identification of hazards due to sea-level rise and se-
vere storms, the assessment of consequences of such storm
events, monitoring and shore protection, coastal archaeology,
resource management and marine spatial planning, anthro-
pogenic sensitivity and environmental status assessment, and
other scientific research.
6.4.4 Underwater archaeology
In the last 25 years, terrestrial archaeology has largely bene-
fitted from remote sensing tools and methods (McCoy and
Ladefoged, 2009). Radar and lidar data have helped re-
veal archaeological features of interest in many areas of the
world (e.g. Meylemans et al., 2015), or detect anomalies
that could be linked to sites of interests that cannot be seen
from the ground (e.g. Lin et al., 2011). Geomorphometry
has been used on radar and lidar data to identify such pat-
terns (Kvamme, 1999) or to describe particular areas (Tur-
rero et al., 2013). Similarly to what happens in marine geo-
morphometry, its use is often not being recognized as geo-
morphometry or terrain analysis.
The remote sensing techniques described in Sect. 2 have
also been used in underwater archaeology to collect bathy-
metric and backscatter data that were used, for instance, in
initial investigations of wreck site locations and extent be-
fore divers or ROVs further investigate the sites (e.g. Jones
et al., 2005; Masetti and Calder, 2012). Similarly to terres-
trial archaeology, these types of data enable both the direct
identification of the features on the seafloor or anomalies that
may indicate potentially buried artefacts (Papatheodorou et
al., 2005). Geomorphometry has yet to gain traction in un-
derwater archaeology, but is not completely absent. Using
MBES data, Stieglitz (2012) documented an area of seafloor
off Australia that had a conspicuous arrangement of over
1200 shallow holes, and wide (up to 10 m) and deep (up to
1.5 m) holes. They classified these holes using local slope
measurements, and found that the systematic distribution of
these seafloor features was related to their distance from a
shipwreck and likely caused by bioturbation. In another ap- Hydrol. Earth Syst. Sci., 20, 3207–3244, 2016
3228 V. Lecours et al.: A review of marine geomorphometry
plication, Passaro et al. (2013) extracted archaeological fea-
tures related to Italian sunken cities using curvatures and the
r.param.scale command from GRASS GIS (cf. Sect. 5.1).
Slopes values were used by Solsten and Aitken (2006) to as-
sess the risk of disturbance of archaeological sites by mass
movement and marine flooding in Nunavut, Canada. The ap-
plication of techniques from geomorphometry to underwater
archaeology is likely to increase in the future, and we note
the potential of seismic geomorphometry to assist in the in-
vestigation of buried artefacts.
7 The future of marine geomorphometry
7.1 Current and future trends in marine
Current developments in the marine geomorphometry liter-
ature are primarily focused on the data acquisition end of
the workflow. Technology and equipment for surveying the
seafloor are improving in quality, accuracy, and cost effec-
tiveness, which will allow for an increase in data availability
and quality. In coastal environments, ongoing research is fo-
cused on improving the extraction of depth information in
the littoral gap in order to create seamless DTMs from the
seafloor to land, and developments in data fusion should soon
enable broadscale geomorphometric analyses of coastal envi-
ronments. As the pressure on coastal environments increases,
such information will become crucial for many applications.
From an ecosystem point of view, coastal environments are
also very rich in biodiversity. Studies of the topographic
structure that can be identified from CTMs using geomor-
phometric techniques are likely to facilitate a better scientific
understanding of these ecosystems. In the deep sea, extensive
use of AUV- and ROV-based MBES and other technologies
means we are now able to collect high-resolution bathymetric
data of environments never explored before at such level of
detail. The knowledge that has been gained from using these
data in combination with different techniques, including geo-
morphometry, has revolutionized scientific understanding of
many marine environments. It was initially thought that the
deep sea was mostly flat, muddy and lifeless, but the last 20
years of research have proven otherwise. Nevertheless, ex-
ploration is far from complete; there are still wide gaps in
the scientific knowledge of deep-sea patterns, processes, and
ecosystems. High quality bathymetric data are fundamental
to the success of revealing this knowledge and its limited
availability is currently a barrier to effective protection and
management of vulnerable species (Vierod et al., 2014; Ross
et al., 2015).
As the marine geomorphometry community moves for-
ward, it will rapidly need to start addressing issues other
than those associated with data acquisition. The availability
of tools that streamline the workflow from data collection to
analysis will be key in making a more complete science of
marine geomorphometry accessible to marine scientists with
a wide range of background and experience. Repositories of
comprehensive and freely available data sets and tools, such
as Digital Coast that provide free coastal and marine bathy-
metric data and analytical tools (NOAA, 2016), are the way
forward to improve accessibility to the wider scientific com-
munity, and this may well mean that bathymetric data gain
the attention of those currently engaged in developing geo-
morphometric methods for terrestrial data. We also acknowl-
edge that easily accessible GIS tools and readily available
data can also bring hidden dangers from non-critical use by
users with limited appreciation of data collection and pro-
cessing methods, which to the expert clearly reflect the lim-
itations in the utility of particular bathymetric data sets. To
prevent this danger of inappropriate use, tools and data sets
need to be accompanied by complete metadata that include
information on data provenance, survey, scale, error, and un-
certainty quantification, and any other information relevant
to further use of the tools and data sets. Metadata are cru-
cial to create a “quality-aware” community (Devillers et al.,
2007; Lecours et al., 2015b). The use of the CUBE algorithm
to create BASE surfaces is one way to carry over a measure
of quantified uncertainty of the data, but such information
is not readily available for the majority of publicly available
data sets. This type of information needs to become more ac-
cessible to marine scientists with a broad range of scientific
It is becoming critical to raise awareness of geomorphom-
etry in the marine science community. Methods from spe-
cific geomorphometry demonstrate a lot of potential for ma-
rine applications and should be used more extensively. At
the same time, it is opportune to improve practices by set-
ting standards and protocols for the application of geomor-
phometry. Methods and interpretations need to be standard-
ized, particularly in view of issues specific to the marine en-
vironment, or where data and analyses behave differently un-
derwater than on land. Amongst these, the influence of scale
and data resolution on the results, and the consideration of
spatial uncertainty should be prioritized. End users should
be explicit about which algorithms or methods they use and
at which scale in order to enable proper comparison among
studies. Since geomorphometric analyses are more and more
performed within GIS environments, devising a GIS-based
standard methodology and symbology for marine geomor-
phological mapping using geomorphometry would be a very
useful goal for the marine geomorphometry community. Ulti-
mately, the type of standards and protocols a marine geomor-
phometry community could develop should encourage wider
applications of bathymetric data and allow marine scientists
optimize the use of their expensive data sets.
7.2 Uniting efforts in geomorphometry
This manuscript has discussed the current practices in ma-
rine geomorphometry, from data collection to the applica-
tions. Through all aspects of this discussion it is apparent
Hydrol. Earth Syst. Sci., 20, 3207–3244, 2016
V. Lecours et al.: A review of marine geomorphometry 3229
that the use of modern geomorphometric techniques in the
marine realm is relatively nascent, having begun only over
a decade ago in application areas outside of marine geomor-
phology. The dramatic increase in DBM availability, com-
bined with the increasingly accessible and user-friendly GIS
tools, is currently fuelling the amount and diversity of ap-
plications of marine geomorphometry. However, this avail-
ability can become a double-edge sword. As noted by Dolan
and Lucieer (2014), “Although a [DBM] is a model of the
seabed surface, it is often not treated as a model but rather
is accepted as a true representation of the seabed”. Further-
more, as highlighted in Fig. 2, the end users of geomorpho-
metric techniques are not always aware that they are actually
“doing” geomorphometry, but rather think of the steps they
are performing as simply using GIS tools for data analysis.
As the number of applications increases, some of the fun-
damental issues associated with marine geomorphometry are
not being addressed quickly or broadly enough. This can in-
crease the risk of unsuspecting end users misusing data or
techniques, and to the misinterpretation of results. For in-
stance, due to a lack of awareness of the impact of artefacts
in DBMs and their propagation to terrain attributes, artefacts
are often disregarded, or assumed to be obvious. In habitat
mapping, the consequences of artefacts are often apparent to
geomorphometry-aware users in the final maps (e.g. Zieger
et al., 2009; Lucieer et al., 2012), but this can become prob-
lematic if the maps are being used in conservation and man-
agement decision making if the effect of the artefacts is not
appreciated by the end user. It is thus crucial for end users,
planners, managers, and decision makers to become aware
and understand the properties of their data that result from
each of the five steps of geomorphometry, and how these
properties influence their particular application.
In addition to end users not being aware of geomorphom-
etry as a science, scientists engaged in more terrestrial and
extra-terrestrial geomorphometry are rarely aware of ma-
rine geomorphometry, its differences, and its similarities with
their field of expertise. For example, at the turn of the mil-
lennium, Pike (2000) identified the study of seafloor abyssal
hills as a prospect topic for the application of geomorphom-
etry. However, many examples can be found of marine geo-
physics and geomorphology studies dating from the 1980s–
1990s that have used geomorphometry in abyssal hills (e.g.
Malinverno and Gilbert, 1989; Goff, 1991, 1992; Malinverno
and Cowie, 1993; Shaw and Lin, 1993). A decade later,
Pike et al. (2009) tried to suggest using digital depth mod-
els (DDM) to characterize surface models of the seafloor, de-
spite the wide acceptance of DBM as an appropriate term in
the marine geomorphometry literature.
We recognize a critical need for a dedicated scientific ef-
fort in marine geomorphometry that will address, and raise
awareness of the fundamental issues related to marine geo-
morphometry. This effort does not necessarily have to come
solely from the marine science community, indeed it may
well benefit from the expertise of many of those scientists
already engaged in terrestrial geomorphometry. The main
objectives of this effort would be to learn from the lessons
of terrestrial geomorphometry, ensure that studies of geo-
morphometry become more widespread in the marine liter-
ature, and respond to the challenges and opportunities for a
wider adoption of marine geomorphometry as a key tool in
marine sciences, whilst improving and upholding scientific
standards. Since sub-fields of geomorphometry dealing with
different types of environments are ultimately parts of the
same science and share more similarities than have differ-
ences, these standards should become common to all these
sub-fields. For example, geomorphometry is a field recog-
nized for its ambiguous terminology, particularly in terms of
terrain attributes (Bishop et al., 2012). The field of geomor-
phometry should move towards a more uniting terminology
and vocabulary across environments that would reduce some
of that ambiguity. For instance, the use of the terms DEM,
DBM, CTM, and DDM should be abandoned in favour of
the more neutral, all-encompassing term DTM. Moving to-
wards a joint terminology is just an example of how we can
reunite all sub-fields of geomorphometry together, with com-
mon goals and approaches. For instance, many of the issues
and future challenges mentioned in this overview (e.g. un-
certainty and error propagation and modelling, scale, change
detection) have been discussed in recent reviews about terres-
trial high-resolution topographic data and Earth surface pro-
cesses (Tarolli, 2014; Passalacqua et al., 2015), highlighting
the similarities in challenges and opportunities that marine
and terrestrial geomorphometry are facing. Uniting efforts in
geomorphometry will likely result in more effective research
and development and facilitate the coupling with other disci-
plines, including different fields of marine sciences.
8 Conclusions
Relative to the “young” and “still forming” modern terres-
trial geomorphometry (Evans and Minár, 2011, p. 105), the
use of geomorphometry in the marine realm is still in its in-
fancy. Ever since the first coarse-scale DBMs were gener-
ated, marine geomorphometry has helped improve scientific
understanding of the oceans, from the relatively thin border
where land meets sea to the deepest waters. This paper is
timely because it provides an overview of the state of the art
in the field and discusses standards for the applications of
marine geomorphometry. By following the five main steps
of geomorphometry by Pike et al. (2009), we have reviewed
marine geomorphometry in a way that can easily be com-
pared with terrestrial geomorphometry. We have provided an
overview of the different methods to sample the depth of the
seafloor, the interpolation methods and issues of spatial scale
associated with the generation of a DBM, as well as dis-
cussing the different errors and artefacts that are character-
istic of DBMs but different from those common in DEMs.
Further, we have discussed how general and specific geo-
morphometry are applied underwater, provided applications Hydrol. Earth Syst. Sci., 20, 3207–3244, 2016
3230 V. Lecours et al.: A review of marine geomorphometry
of marine geomorphometry, and outlined future trends in the
field. Clearly there is room in the literature for more detailed
reviews of each of these five steps and relating to many of
the sub-disciplines; however, we hope that this review will
serve as a solid foundation for further, more detailed reviews
on these sub-topics.
Based on this review, we provide the following recommen-
dations that should help establish more productive practices
in marine geomorphometry: (1) errors and spatial uncertainty
should be quantified so that they are able to be considered
in the geomorphometric analyses and in the interpretation
of results; (2) metadata should consistently be associated
with data sets to explicitly indicate data provenance, qual-
ity (i.e. quantification of uncertainty), and the spatial scale at
which the data set was intended to be used; (3) data, meta-
data and tools should be made available for a wider applica-
tions of bathymetric data; (4) standardization of methods and
interpretations for each field of application should be docu-
mented, particularly in view of the influence of algorithms,
scale and data resolution on the results; and (5) a GIS-based
standard symbology for marine geomorphological mapping
based on geomorphometry should be devised.
Through raised awareness of each other’s disciplines, we
hope that both marine scientists and geomorphometry prac-
titioners will be better placed to work together in addressing
the fundamental issues of marine geomorphometry, whilst
upholding scientific standards in marine spatial analysis.
Building a dedicated effort in marine geomorphometry that
can draw on lessons learned in terrestrial geomorphometry
will not only encourage marine applications and continued
scientific development, but will ensure that the science of ge-
omorphometry is used to its full potential for studying the
topography of the whole planet.
Hydrol. Earth Syst. Sci., 20, 3207–3244, 2016
V. Lecours et al.: A review of marine geomorphometry 3231
Appendix A: Details on the theories behind bathymetric
data collection techniques
A1 Satellite radar altimetry
Radar altimeters emit microwaves that bounce on the sea sur-
face and return to the receiver, giving the altitude of the satel-
lite over the sea surface. The topography of the surface can
then be deduced and used to derive ocean circulation patterns
(Fu, 1983) or geoid models (e.g. Fernandes et al., 2000). The
geoid represents the gravitational equipotential surface of the
Earth; gravity varies in space, and the anomalies in its distri-
bution were found to be correlated with bathymetry (McKen-
zie and Bowin, 1976; Watts 1979). Despite initial reports
stating that it was impossible to derive reliable bathymetry
from satellite altimeter (Keating et al., 1984; Watts and Ribe,
1984), Dixon et al. (1983) were the first to demonstrate its
feasibility using real data. Several algorithms and methods
to estimate and predict bathymetry from the gravitational
field have since been developed (reviewed in Calmant and
Beaudry, 1996 and Sandwell and Smith, 2001). However,
it remains a complex process (Calmant and Beaudry, 1996)
that still requires acoustic data for calibration (Smith and
Sandwell, 1997). Altimetry-derived bathymetric data only
provide low resolution estimates of the bathymetry; ocean
waves create a lot of noise that prevents the collection of
fine-resolution data (Smith, 1998), and rough seafloor geol-
ogy affects finer-scale data accuracy (Smith and Sandwell,
A2 Optical remote sensing
The ability to derive depth estimates from imagery comes
from the optical Beer-Lambert law of light absorbance,
which describes light absorption in uniformly attenuating
water (Serway and Beichner, 1983). In water, light gets ab-
sorbed exponentially as depth increases (Lyzenga, 1978).
The Beer–Lambert law allows the mathematical derivation
of depth estimates from the brightness values of pixels in
an image, when the absorption characteristics of an area are
known (Mobley et al., 2005; Carbonneau et al., 2006). Since
the absorption rate of an area is dependent on water turbid-
ity and the characteristics of the incoming energy (e.g. the
intensity, angle and wavelength of sunlight), calibration with
ground-truth data (i.e. measurements of local light absorp-
tion characteristics) is a key step in the application of this
method. However, calibration is made difficult by temporal
variations in the illumination characteristics of an area (Car-
bonneau et al., 2006); the calibration data would ideally need
to be collected at the same time as the remotely sensed data
to ensure identical environmental conditions. Since reliable
calibration data are particularly challenging to obtain in ma-
rine waters (Lafon et al., 2002; Dekker et al., 2011), some
methods have been proposed to estimate bathymetry without
ground-truth data (e.g. Fonstad and Marcus, 2005), however
these are not yet widely adopted (Feurer et al., 2008). Rather
than using the level of absorbed energy to derive bathymetry
from imagery, some authors, e.g. Maritorena et al. (1994),
have used bottom reflectance, which is the level of reflected
energy. Many types of imagery have been used to derive ma-
rine bathymetry: hyperspectral (e.g. Ma et al., 2014), mul-
tispectral (e.g. Lyzenga et al., 2006; Pacheco et al., 2015),
broadband colour (e.g. Westaway et al., 2003) and grayscale
images (e.g. Winterbottom and Gilvear, 1997). Multispectral
images enable refined depth estimates by extracting informa-
tion on the bottom types from non-visible spectral bands; ac-
counting for bottom reflectance allows better distinguishing
the spectral response from which depth estimates are derived
(Winterbottom and Gilvear, 1997). The red band of the elec-
tromagnetic spectrum is particularly successful in detecting
depth variations (Legleiter et al., 2004).
A3 Acoustic remote sensing
Acoustic waves are the most practical vehicle of informa-
tion in the submarine environment; since water is denser than
air, acoustic vibrations propagate through water four to five
times quicker (Lurton, 2010). Sound also travels greater dis-
tances underwater as there is less attenuation in the water
compared to air (Lurton, 2010). Sidescan sonars (SSS) –
developed for military applications in the 1940s – , single-
beam echosounders (SBES), and multibeam echosounders
(MBES) are all active sonars that transmit a characteris-
tic and controlled signal in direction of the seafloor. When
knowing the speed of sound in water, the two-way travel
time - the time taken for acoustic waves to travel between
the source and the seafloor and back to the source again -
can be measured to estimate the range between the target
and the sonar, thus enabling deduction of depth. The inten-
sity of this return (i.e. backscatter) can also be measured to
provide information on the properties of the seafloor (e.g.
sediment composition). Despite their ability to provide in-
formation on the topographic roughness and hardness of the
seafloor, SSS cannot reliably measure seafloor relief directly,
except when two receiving antennas are combined and prin-
ciples of interferometry are applied to create bathymetric es-
timates. Synthetic aperture sonars differ from traditional SSS
by utilising data from several consecutive pings to synthesize
a longer sonar array capable of measuring at higher resolu-
tion. Many modifications of SBES were suggested through
time to increase their performance. For instance, the split-
beam echosounder uses interferometry to improve the accu-
racy of the data and was used to determine slope directly
(Fosså et al., 2005). The dual-beam echosounder uses two
beams of different aperture oriented in the same direction to
locate targets more accurately. Finally, the sweep sounder is
a combination of several SBES mounted on a horizontal sup-
port to increase the number of soundings. Hydrol. Earth Syst. Sci., 20, 3207–3244, 2016
3232 V. Lecours et al.: A review of marine geomorphometry
A4 Bathymetric lidar
Lidar systems are active sensors that generate laser beams; by
knowing the speed of light and measuring the time between
when a beam was sent and when its corresponding returns
came back to the sensor, it is possible to derive the distance
between the sensor and the different targets (e.g. land, water
surface, seabed) encountered by the laser beams (Irish and
Lillycrop, 1999). Principles and the geometry of bathymetric
lidar systems are reviewed in Kashani et al. (2015).
Hydrol. Earth Syst. Sci., 20, 3207–3244, 2016
V. Lecours et al.: A review of marine geomorphometry 3233
Acknowledgements. V. Lecours thanks Emma LeClerc for her valu-
able comments on sections of this manuscript, Rodolphe Devillers
for the insightful discussions about marine geomorphometry, and
the Natural Sciences and Engineering Research Council (NSERC)
of Canada for providing funding. A. Micallef is funded by a Marie
Curie Career Integration Grant PCIG13-GA-2013-618149. We also
acknowledge Memorial University Libraries for financial support,
and the three referees and the editor for their comments.
Edited by: H. Mitasova
Reviewed by: N. Mitchell and two anonymous referees
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... The shape, orientation, distribution and depth of mapped seabed Morphology Features (Part 1: Dove et al., 2020) can be used alongside subsurface data (e.g. sub-bottom profiles, cores) to interpret seafloor geomorphology (Goudie, 2006;Lecours et al., 2016). Such detailed interpretations can be extrapolated to similar mapped units within a study area to produce geomorphology maps where the mapped units are intrinsically linked to their geological history. ...
... This includes an overview of many of the commonly used tools for computing such information in quantitative form. Lecours et al., (2016) recently reviewed terrain attributes, including special reference to geomorphology, in the wider context of marine geomorphometry. Issues such as the underlying bathymetric data resolution, quality, as well as the choice of algorithm and analysis distance will affect both the values obtained, their uncertainty and usefulness in comparative studies (Dolan and Lucieer, 2014;Lecours et al., 2017;Misiuk et al., 2021). ...
... The study of the shape of the Earth's surface and the processes forming them (modified from Harris and Baker, 2011). This may include, but is distinct from, geomorphometry -the science of quantitative terrain characterization, which encompasses acquisition and processing of topographic data as well as analyses and applications related to geomorphology (see Lecours et al., 2016). ...
Technical Report
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Maps of seabed geomorphology derived from bathymetry data provide foundational information that is used to support the sustainable use of the marine environment across a range of activities that contribute to the Blue Economy. The global recognition of the value of the Blue Economy and several key global initiatives, notably the Seabed 2030 project to map the global ocean and the United Nations Decade of Ocean Science for Sustainable Development, are driving the proliferation and open dissemination of these data and derived map products. To effectively support these global efforts, geomorphic characterisation of the seabed requires standardised multi-scalar and interjurisdictional approaches that can be applied locally, regionally and internationally. This document describes and illustrates a geomorphic lexicon for the full range of coastal to deep ocean geomorphic Settings and related Processes that drive the formation, modification and preservation of geomorphic features on the seabed. Terms and Settings/Processes have been selected from the literature and structured to balance established terminology with the need for consistency between the range of geomorphic environments and processes. This document also presents a glossary of 406 geomorphic terms and identifies the insights that can be gained by mapping each unit type, from an applied perspective.
... After processing the bathymetric data, a set of high-quality data with which we carried out a Digital Terrain Model (DTM) was obtained with a 3 cm resolution, and we were ready for the measurement of the structural elements that can be distinguished from the shipwreck as well as for the comparison with another DTM obtained from a different source of data, in our case properly treated photographic images, as already indicated. DTMs using bathymetric data are hereafter referred to as Digital Bathymetric Models (DBM) to distinguish them from Digital Elevation Models (DEM), a term usually reserved for terrestrial elevation data [46]. Finally, the data density of the blocks was very good, meeting the IHO standards for a Special Order survey due to the overlap that was made during the acquisition. ...
... The measures in the DBM are shown in Table 2 After processing the bathymetric data, a set of high-quality data with which we carried out a Digital Terrain Model (DTM) was obtained with a 3 cm resolution, and we were ready for the measurement of the structural elements that can be distinguished from the shipwreck as well as for the comparison with another DTM obtained from a different source of data, in our case properly treated photographic images, as already indicated. DTMs using bathymetric data are hereafter referred to as Digital Bathymetric Models (DBM) to distinguish them from Digital Elevation Models (DEM), a term usually reserved for terrestrial elevation data [46]. Finally, the data density of the blocks was very good, meeting the IHO standards for a Special Order survey due to the overlap that was made during the acquisition. ...
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Over the last few years, due to various climatic, anthropogenic, and environmental factors, a large amount of submerged heritage has been unearthed and exposed to deterioration processes in the Bay of Algeciras. These impacts can be more severe in shallow waters, where the cultural heritage is more vulnerable to natural and human-induced impacts. This makes it urgent to document cultural heritage at risk of disappearing using different techniques whose efficiencies in the archaeological record need to be determined and compared. For this purpose, we have documented a shipwreck in the Bay of Algeciras using two techniques: photogrammetry and a multibeam echosounder. The photogrammetric method consists of obtaining a 3D model from numerous photographs taken of an object or a site. The processing software creates three-dimensional points from two-dimensional points found in the photographs that are equivalent to each other. Multibeam echosounders are capable of providing side scan imagery information in addition to generating contour maps and 3D perspectives of the surveyed area and can be installed in an unmanned surface vehicle. As a result, we have obtained two 3D visualisations of the shipwreck, i.e., digital copies, that are being used both for the analysis of its naval architecture and for its dissemination. Through the comparison of the two techniques, we have concluded that while a multibeam echosounder provides a detailed digital terrain model of the seabed, photogrammetry performed by divers gives the highest resolution data on objects and structures. In conclusion, our results demonstrate the benefits of this combined approach for accurately documenting and monitoring shipwrecks in shallow waters, providing valuable information for conservation and management efforts.
... Digital terrain models (DTMs) are datasets containing altitude values above or below a reference level, such as a reference ellipsoid or a tidal datum over geographic space, often in the form of a regularly gridded raster, which comprise values stored within regularly gridded "cells" or "pixels" (Katzil & Doytsher, 2000;Krcho, 1999;Lecours et al., 2016). ...
... Terrain attributes that describe the shape and character of the Earth's surface can be calculated from these DTMs. While a large number of attributes can be found in the literature (Wilson, 2018), the most common terrain attributes can generally be categorized into five thematic groups: slope, aspect, curvature, relative position, and roughness (Bouchet et al., 2015;Lecours et al., 2016Lecours et al., , 2017Wilson et al., 2007) (Figure 1). While this framework is by no means the only way to group terrain attributes, it provides a useful way to organize related terrain attributes based on their physical meaning and will be used within this article for organizational purposes. ...
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Digital terrain models (DTMs) are datasets containing altitude values above or below a reference level, such as a reference ellipsoid or a tidal datum over geographic space, often in the form of a regularly gridded raster. They can be used to calculate terrain attributes that describe the shape and characteristics of topographic surfaces. Calculating these terrain attributes often requires multiple software packages that can be expensive and specialized. We have created a free, open-source R package, MultiscaleDTM, that allows for the calculation of members from each of the five major thematic groups of terrain attributes: slope, aspect, curvature, relative position, and roughness, from a regularly gridded DTM. Furthermore, these attributes can be calculated at multiple spatial scales of analysis, a key feature that is missing from many other packages. Here, we demonstrate the functionality of the package and provide a simulation exploring the relationship between slope and roughness. When roughness measures do not account for slope, these attributes exhibit a strong positive correlation. To minimize this correlation, we propose a new roughness measure called adjusted standard deviation. In most scenarios tested, this measure produced the lowest rank correlation with slope out of all the roughness measures tested. Lastly, the simulation shows that some existing roughness measures from the literature that are supposed to be independent of slope can actually exhibit a strong inverse relationship with the slope in some cases.
... This may have potential implications for how bathymetry is interpreted, but they have yet to be explored. These implications can be far-reaching as bathymetry is the primary input for marine geomorphometric analyses that have become common in disciplines like marine habitat mapping, hydrodynamic modelling, and submarine geomorphology [4]. ...
Conference Paper
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In recent years, new multibeam echosounders that can simultaneously collect data at multiple frequencies have become available. However, the effects of acoustic frequency on bathymetric data have yet to be characterized, as early research on these new systems has instead focused on backscatter data. Here we explore such effects by deriving terrain attributes and classifications from bathymetric data from Head Harbour, Nova Scotia, Canada, that were collected at five different operating frequencies. The geomorphometric analyses were conducted on bathymetric surfaces generated from data collected at each operating frequency using four scales of analysis. Results show that bathymetry, its derived terrain attributes, and terrain classifications produced with them are all dependent on the acoustic frequency used to collect bathymetric data. While the observed effects on the regional bathymetry were relatively minor, local bathymetry, terrain attributes and terrain classifications were highly impacted by the frequency used when collecting data. The impacts were less important when the terrain attributes and classifications were generated using broader scales of analysis. These results raise questions about how bathymetry is measured and defined and how we should interpret the outcomes of marine geomorphometric analyses. This is particularly relevant as such analyses have become a key component of marine habitat mapping and submarine geomorphology mapping.
... Furthermore, FCNNs can leverage semisupervised strategies whereby subsets of labelled data are used for optimisation; this approach can be beneficial for practical applications of FCNNs for marine geomorphology and ecology mapping where the quantity and distribution of labelled data may be limited due to associated costs of in situ surveying (Leitão et al., 2018;Hobley et al., 2021). While interest in DL has been shown early on by the marine community for ecological and habitat mapping (Gazis et al., 2018;Yasir et al., 2021), only a few studies have been focused on automated identification with DL of seabed geomorphological features or textures (McClinton et al., 2012;Valentine et al., 2013;Juliani, 2019;Keohane and White, 2022;Lundine et al., 2023), even though the significance of geomorphology for habitat distribution is widely acknowledged (Brown et al., 2011;Lecours et al., 2016;Harris and Baker, 2020). Deep Learning in geomorphology has found instead a more fertile ground in coastal and geohazard studies (Ma and Mei, 2021;Buscombe et al., 2023), and in outer space, in particular for Martian or Lunar geology, where several studies have taken advantage of the high resolution optical imagery available and attempted to separate specific landforms from a background (Foroutan and Zimbelman, 2017;Palafox et al., 2017;Wang et al., 2017;Rubanenko et al., 2021), or more generally characterise the ground surface to identify optimal landing spots or assess rover traversability (Wilhelm et al., 2020;Barrett et al., 2022). ...
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In this study we applied for the first time Fully Convolutional Neural Networks (FCNNs) to a marine bathymetric dataset to derive morphological classes over the entire Irish continental shelf. FCNNs are a set of algorithms within Deep Learning that produce pixel-wise classifications in order to create semantically segmented maps. While they have been extensively utilised on imagery for ecological mapping, their application on elevation data is still limited, especially in the marine geomorphology realm. We employed a high-resolution bathymetric dataset to create a set of normalised derivatives commonly utilised in seabed morphology and habitat mapping that include three bathymetric position indexes (BPIs), the vector ruggedness measurement (VRM), the aspect functions and three types of hillshades. The class domains cover ten or twelve semantically distinct surface textures and submarine landforms present on the shelf, with our definitions aiming for simplicity, prevalence and distinctiveness. Sets of 50 or 100 labelled samples for each class were used to train several U-Net architectures with ResNet-50 and VGG-13 encoders. Our results show a maximum model precision of 0.84 and recall of 0.85, with some classes reaching as high as 0.99 in both. A simple majority (modal) voting combining the ten best models produced an excellent map with overall F1 score of 0.96 and class precisions and recalls superior to 0.87. For target classes exhibiting high recall (proportion of positives identified), models also show high precision (proportion of correct identifications) in predictions which confirms that the underlying class boundary has been learnt. Derivative choice plays an important part in the performance of the networks, with hillshades combined with bathymetry providing the best results and aspect functions and VRM leading to an overall deterioration of prediction accuracies. The results show that FCNNs can be successfully applied to the seabed for a morphological exploration of the dataset and as a baseline for more in-depth habitat mapping studies. For example, prediction of semantically distinct classes as “submarine dune” and “bedrock outcrop” can be precise and reliable. Nonetheless, at present state FCNNs are not suitable for tasks that require more refined geomorphological classifications, as for the recognition of detailed morphogenetic processes.
... Information on dominant substrate type has been shown to be related to the distribution of various 2 R. R. McDonald et al. species (e.g. Lacharité and Brown, 2019 ;Wilson et al., 2021 ;Jackson-Bué et al., 2022 ) and significant efforts are underway to map the seafloor (e.g. Brown et al., 2011Brown et al., , 2012Lecours et al., 2016 ;Lacharité et al., 2018 ;Wilson et al., 2021 ). The resulting mapping products can be used to identify different bottom habitats, which can then be incorporated directly into the assessment of habitat-dependent species. ...
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Recent efforts in ocean mapping of seafloor habitat have made data increasingly available. For bottom-dwelling and/or sessile species, there is often a strong relationship between population productivity and habitat, and stock assessment models are likely to be improved by the inclusion of habitat. Here, we extend a recently developed spatio-temporal biomass dynamics model to allow habitat to inform probabilities of non-zero tows and catchability. Simulation experiments demonstrate the ability of this new approach to reliably capture population trends over time and space, with the applicability of the method further demonstrated using data from the Canadian Maritimes Inshore Sea Scallop Fishery in the Bay of Fundy. This habitat-informed spatio-temporal biomass dynamics model better captures underlying processes, reduces uncertainty, thereby improving our understanding of stock status from which fisheries management decisions can be based.
... La geomorfometría obtenida en este trabajo, se realizó de acuerdo con los cinco pasos descritos en Lecours et al. (2016). A continuación, se mencionan brevemente: ...
Technical Report
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Resumen ejecutivo (English below) Se presentan los resultados de un crucero de investigación en el sur de las grandes islas en el Golfo de California realizado del 16 al 30 de septiembre 2021. En esta campaña se utilizaron dos tipos de muestreo. En el primero se realizó la prospección del ecosistema bentónico y pelágico alrededor de formaciones submarinas llamadas Monte Altos Vírgenes y Monte Reforma, como sitios de productividad biológica de gran valor para el ecosistema marino, con profundidades entre 331 y 742 metros, para ello se operó un vehículo vía remota (ROV por sus siglas en inglés). Con una cobertura de 16.5 mn2, donde se generó una batimetría en ambos montes; ubicando dos inmersiones en la cima y dos en la ladera de cada estructura, así como dos estaciones con CTD, donde se realizó la caracterización del ambiente pelágico y bentónico mediante videograbaciones en las que se observaron tunicados, medusas, octocorales, crustáceos, estrellas de mar, tiburones de profundidad Cephalurus cephalus y merluzas del Pacífico Merluccius productus. El segundo muestreo fue para calamar gigante Dosidicus gigas, en los litorales de Baja California Sur y Sonora. En 14 lances de pesca de arrastre de media agua entre 16.8 a 27.4 m de profundidad, en un área barrida de 0.92 km2, se obtuvo una captura total de 148.7 kg y 1,332 individuos, correspondientes a 15 taxones de tres grupos taxonómicos: moluscos, elasmobranquios y peces óseos. Para el calamar se registraron 2.264 kg y una densidad de 448 ind/km2, con una talla promedio de 140.36 mm LM (90-240 mm). Se capturaron también pelágicos menores como anchoveta norteña Engraulis mordax, sardina japonesa Etrumeus acuminatus, sardina monterrey Sardinops sagax, macarela Scomber japonicus y charrito Trachurus symmetricus, en conjunto sumaron 132.411 kg. La captura de los pelágicos resultó ser muy superior a la de los calamares, con 89.04% respecto a la captura total. Mediante el muestreo de CUFES se identificaron 12 grupos de zooplancton, entre los que destacan copépodos y anfípodos. Finalmente, debido a que en México los estudios en montes submarinos son escasos y fragmentados, los resultados de este informe representan un aporte al conocimiento de estos ambientes donde es posible encontrar especies de interés comercial como la merluza. Palabras clave: ROV, merluza, Monte Reforma, Monte Altos Vírgenes. Abstrac We presente the results from a research cruise was carried out in the south of the large islands in the Gulf of California a from September 16 to 30. Two types of sampling were used in this campaign. In the first, the benthic and pelagic ecosystem was surveyed around submarine formations called Monte Altos Vírgenes and Monte Reforma, as biological productivity sites of great value for the marine ecosystem, with depths between 331 and 742 meters, for which a remote controlled vehicle (ROV). With a coverage of 16.5 mn2, where a bathymetry was generated in both mountains; locating two dives at the top and two on the slope of each structure, as well as two stations with CTD, where the characterization of the pelagic and benthic environment was carried out through video recordings in which tunicates, jellyfish, octocorals, crustaceans, starfish were observed, deep-sea sharks Cephalurus cephalus and Pacific hake Merluccius productus. The second sampling was for giant squid Dosidicus gigas, in the coasts of Baja California Sur and Sonora. In 14 mid-water trawling hauls between 16.8 to 27.4 m deep, in a swept area of 0.92 km2, a total catch of 148.7 kg and 1,332 individuals was obtained, corresponding to 15 taxa from three taxonomic groups: molluscs, elasmobranchs and bony fish. For squid, 2,264 kg and a density of 448 ind/km2 were recorded, with an average size of 140.36 mm LM (90-240 mm). Smaller pelagics such as the northern anchoveta Engraulis mordax, Japanese sardine Etrumeus acuminatus, Monterey sardine Sardinops sagax, mackerel Scomber japonicus and squid Trachurus symmetricus were also caught, together totaling 132,411 kg. The pelagic catch turned out to be much higher than that of squid, with 89.04% of the total catch. Through the CUFES sampling, 12 groups of zooplankton were identified, among which copepods and amphipods stand out. Finally, because in Mexico studies on seamounts are scarce and fragmented, the results of this report represent a contribution to the knowledge of these environments where it is possible to find species of commercial interest such as hake. Keywords: ROV, hake, Monte Reforma, Monte Altos Vírgenes.
... DSMs can provide information characterizing structural traits of oyster reefs which can influence the surrounding physical environment (Chowdhury et al., 2019). DSMs also allow analysis of oyster reefs from a geomorphometric (i.e., the quantitative analysis of land surfaces) perspective by generating terrain attributes such as rugosity and curvature (Hengl & Reuter, 2008;Lecours et al., 2016;Florinsky, 2017). ...
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Eastern oysters (Crassostrea virginica) generate structurally complex reef systems that offer diverse ecosystem services. However, there is limited understanding of how reef structure translates into reef condition. This knowledge gap might be better addressed if oyster reef structure could be more rapidly assessed. Conventional in situ monitoring techniques are often time-intensive, invasive, and do not provide spatially continuous information on the reef structure. Unoccupied Aircraft Systems (UAS), commonly referred to as drones, equipped with optical sensors can rapidly and non-invasively map intertidal oyster reef surfaces. We demonstrate how a digital surface model from UAS-based light detection and ranging (lidar) can enable very high-resolution characterization and monitoring of intertidal oyster reef surface morphology. Generalized linear models (GLMs) identified relationships between in situ live oyster counts and surface complexity metrics derived from digital surface models produced from lidar point clouds. Statistically significant relationships between surface complexity metrics (e.g., gray level co-occurrence features, volume to area ratio, skewness of elevation) and live oyster counts suggest that surface complexity provides useful proxies for reef condition. Advancing the application of remote sensing to intertidal oyster reefs can help identify reefs that are prone to degradation and inform conservation and restoration strategies.
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Morphometric diversity is an important component of overall seabed geodiversity. Automated methods for classification of morphometric features (ridges, peaks, valleys etc.) provide a convenient way of classifying large volumes of data in a consistent and repeatable way and a basis for assessing morphometric diversity. Here, we apply ‘geomorphons’, a pattern recognition approach to morphometric feature classification, to 100 m resolution multibeam bathymetry data in the Barents and Norwegian Seas, Norway. The study area spans depths from a few metres to nearly 6000 m across several geological settings. Ten unique morphometric features are delineated by the geomorphon analysis. From these results, we compute the variety of features per 10 km2. This simple ‘geomorphon richness’ measure highlights broad-scale morphometric diversity across the study area. We compare the richness results with terrain attributes and across physiographic regions. Our results provide new regional insights, which together with more detailed information will help guide follow-up surveys as well as identifying diversity hotspots, which may require special management.
Conference Paper
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Coastal scientists recognize that while it is critical to quantify volumetric and repeatable spatio-temporal change near tidal inlets, various physical and morphological factors make these areas perhaps the most challenging regions to map. It has been well established that traditionally spaced transects perpendicular to the morphology do not accurately represent complex terrain when interpolated into a digital elevation model (DEM), regardless of gridding algorithm. Purpose-built acquisition platforms paired with modern singlebeam, multibeam and topographic instrumentation are necessary for accurate surveys of this environment. Equally important to the accuracy of the final DEM is survey planning such that potential sources of survey uncertainty are minimized. Remote sensing techniques and observational data (e.g., aerial photography, nautical charts and existing data) are used to develop predictions of the anticipated terrain. Customized survey designs increase survey efficiency and allow users more flexibility in gridding methods and contribute to the overall accuracy of the modeled surface geometry. The selection of gridding parameters depends largely on the spatial distribution of the input data, resolution of the output grid and the characteristics of the modeled terrain. Survey design, instrumentation and interpolation methods are of principal importance when constructing accurate and seamless topo/bathy elevation models of these challenging environments.
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This book focuses on GIS-based processing, analysis, and visualization of coastal LiDAR time-series. The descriptions of the approaches outlined here are accompa- nied by examples, which are implemented using the open source GIS, GRASS and freely available sample data.
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Toolbox for ArcGIS built from results of Lecours et al. (2017) and validated in Lecours et al. (2016). This toolbox generates six independent terrain attributes that together summarize topographic or bathymetric variability. References: Lecours, V., Devillers, R., Simms, A.E., Lucieer, V.L., and Brown, C.J. (2017) Towards a framework for terrain attribute selection in environmental studies. Environmental Modelling & Software, 89:19-30. Lecours, V., Brown, C.J., Devillers, R., Lucieer, V.L., and Edinger, E.N. (2016) Comparing selections of environmental variables for ecological studies: a focus on terrain attributes. PLoS ONE, 11:e0167128. TO CITE: v. 1.1: Lecours, V. (2017) Terrain Attribute Selection for Spatial Ecology (TASSE) v. 1.0: Lecours, V. (2015) Terrain Attribute Selection for Spatial Ecology (TASSE)
Full-text available
Toolbox for ArcGIS built from results of Lecours et al. (2017) and validated in Lecours et al. (2016). This toolbox generates six independent terrain attributes that together summarize topographic or bathymetric variability. References: Lecours, V., Devillers, R., Simms, A.E., Lucieer, V.L., and Brown, C.J. (2017) Towards a framework for terrain attribute selection in environmental studies. Environmental Modelling & Software, 89:19-30. Lecours, V., Brown, C.J., Devillers, R., Lucieer, V.L., and Edinger, E.N. (2016) Comparing selections of environmental variables for ecological studies: a focus on terrain attributes. PLoS ONE, 11:e0167128. TO CITE: v. 1.1: Lecours, V. (2017) Terrain Attribute Selection for Spatial Ecology (TASSE) v. 1.0: Lecours, V. (2015) Terrain Attribute Selection for Spatial Ecology (TASSE
Now ubiquitous in modern life, spatial data present great opportunities to transform many of the processes on which we base our everyday lives. However, not only do these data depend on the scale of measurement, but also handling these data (e.g., to make suitable maps) requires that we account for the scale of measurement explicitly. Scale in Spatial Information and Analysis describes the scales of measurement and scales of spatial variation that exist in the measured data. It provides you with a series of tools for handling spatial data while accounting for scale. The authors detail a systematic strategy for handling scale issues from geographic reality, through measurements, to resultant spatial data and their analyses. They also explore a process-pattern paradigm in approaching scale issues. This is well reflected, for example, in chapters dealing with terrain analysis, in which scale in terrain derivatives is described in relation to the processing involved in the derivation of specific terrain variables from elevation data, and area classes, which are viewed as driven by class-forming covariates. Lastly, this book provides coverage of some of the issues related to scale that are relatively under-represented in the literature, such as the effects of scale on information content in remotely sensed images, and the interaction between scale and uncertainty that is increasingly important for spatial information and analysis. By taking a rigorous, scientific approach to scale and its various meanings in relation to the geographic world, the book alleviates some of the frustration caused by dealing with issues of scale. While past research has led to an increasing number of journal articles and a few books dedicated to scale modeling and change of scale, this book helps you to develop coherent strategies for scale modeling, highlighting applicability for a variety of fields, from geomatic engineering and geoinformatics to environmental modeling.
All aspects of surface form can be considered to reflect surface roughness. Horizontal variation includes the concepts of texture and grain, while vertical variation is discussed under relief. The relationships between these are embodied in slope and the dispersion of slope magnitude and orientation. The distribution of mass within the elevation range of a topographic surface is described under hypsometry. Parameters for further investigation are selected from these categories after an examination of the relationships among the variables using correlation analysis.
Digital Terrain Analysis in Soil Science and Geology, Second Edition, synthesizes the knowledge on methods and applications of digital terrain analysis and geomorphometry in the context of multi-scale problems in soil science and geology. Divided into three parts, the book first examines main concepts, principles, and methods of digital terrain modeling. It then looks at methods for analysis, modeling, and mapping of spatial distribution of soil properties using digital terrain analysis, before finally considering techniques for recognition, analysis, and interpretation of topographically manifested geological features. Digital Terrain Analysis in Soil Science and Geology, Second Edition, is an updated and revised edition, providing both a theoretical and methodological basis for understanding and applying geographical modeling techniques. Presents an integrated and unified view of digital terrain analysis in both soil science and geology. Features research on new advances in the field, including DEM analytical approximation, analytical calculation of local morphometric variables, morphometric globes, and two-dimensional generalized spectral analytical methods. Includes a rigorous description of the mathematical principles of digital terrain analysis. Provides both a theoretical and methodological basis for understanding and applying geographical modeling.