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

Hydrology and Earth System Sciences

August 2016
20(8):3207-3244

DOI:10.5194/hess-20-3207-2016

License
CC BY 4.0

Authors:

Vincent Lecours

University of Québec in Chicoutimi

Aaron Micallef

Monterey Bay Aquarium Research Institute

Vanessa Lucieer

University of Tasmania

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.

. Continued.

…

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.

…

Illustration of the main types of terrain attributes that can be derived from bathymetry data. Modified after Wilson et al. (2007).

…

Indicative workflow showing the use of terrain attributes in habitat mapping. Generally following some pre-selection of variables the observed habitat points (response variable) are combined with full coverage predictor variables selected from bathymetry, terrain 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 depend 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 visual or physical samples of the seabed. Terrain attributes are typically multi-scale and may include general and/or specific geomorphometry. 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 modelling 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.

…

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).

…

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Hydrol. Earth Syst. Sci., 20, 3207–3244, 2016

www.hydrol-earth-syst-sci.net/20/3207/2016/

doi:10.5194/hess-20-3207-2016

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 (vlecours@mun.ca)

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

written.

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 www.hydrol-earth-syst-sci.net/20/3207/2016/

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

Preprocessing

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)

Applications

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-

try.

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

applications.”

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

www.hydrol-earth-syst-sci.net/20/3207/2016/ 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

geomorphometry.

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 www.hydrol-earth-syst-sci.net/20/3207/2016/

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

Appendix).

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

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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

reconstructions.

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

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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,

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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

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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-

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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),

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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.

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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

Ecological

relevance

Stability of sediments

(ability to live in/on

sediments);

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

structure;

shelter from expo-

sure/predators (link to life

stages);

structural diversity

linked to biodiversity

Geomorphological

relevance

Stability of sediments

(grain size);

local acceleration

of currents (erosion,

movement of sediments,

creation

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

processes

Commonly com-

puted terrain at-

tribute and exam-

ple marine-based

reference

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

eastness/easterness

(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-

ence)

Single scale:

slope: ArcGIS Spatial

Analyst (Horn, 1981)

Single scale:

aspect: ArcGIS Spatial

Analyst (Horn, 1981);

statistical aspect

(northness/eastness);

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)

Multi-scale:

Multiple scale slope:

r.param.scale, Landserf

(Wood, 1996);

Multiscale slope: Land-

serf (Wood, 1996)

Multi-scale:

Multiple scale aspect:

r.param.scale, Landserf

(Wood, 1996);

Multiscale aspect:

Landserf (Wood, 1996)

Multi-scale:

Several measures of

multiple scale curvature:

r.param.scale,

Landserf (Wood, 1996);

Multiple scale BPI

(Lundblad et al., 2006);

Multiscale curvature

Landserf (Wood, 1996)

Multi-scale:

Multiple scale VRM

(Sappington et al.,

2007);

Multiple scale relative re-

lief (Erikstad et al., 2013,

and references

therein);

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

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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-

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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

geohazards

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

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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

General

Volcanic islands

Geomorphology and

geohazards

Coggan and Diesing (2012)

Ezhova et al. (2012)

Continental shelf

Coastal and continental shelf

Habitat mapping and

ecology

Moﬁeld et al. (2004) Continental slope and rise, abyssal hills Hydrodynamics

Passaro et al. (2013) Coastal Others

Morphometric attributes

and their statistical

analyses

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)

General

Continental slope and upper rise

Mid-ocean ridge, abyssal plain, seamounts

General

Continental slope

Mid-ocean ridge

Coastal and inner shelf

Seamount

Seamount, volcanic island

Abyssal hills

Geomorphology and

geohazards

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

Continental slope

Outer shelf

Coastal to inner shelf

Seamounts

Shelf to abyssal plain

Continental slope

Habitat mapping and

ecology

Mohn et al. (2014)

Tong et al. (2013)

Continental slope

Outer shelf

Hydrodynamics

Solsten and Aitken (2006)

Stieglitz (2012)

Coastal

Others

Spectral analysis Fox and Hayes (1985)

Fox (1996)

Gilbert and Malinverno (1988)

Goff and Tucholke (1997)

Moskalik et al. (2014b)

General

Mid-ocean ridge

General

Mid-ocean ridge

Coastal and inner shelf

Geomorphology and

geohazards

Geostatistical methods Herzfeld (1989)

Herzfeld and Higginson (1996)

Ismail et al. (2015)

Continental slope

Mid-ocean ridge

Continental slope

Geomorphology and

geohazards

Diesing et al. (2014) Coastal to inner shelf Habitat mapping and

ecology

Hillman et al. (2015) Continental slope Hydrodynamics

Feature-based

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 shelf

Continental slope

Geomorphology and

geohazards

Calvert et al. (2015) Coastal Habitat mapping and

ecology

Bøe et al. (2015) Continental slope Others

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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

geohazards

Marsh and Brown (2009) Continental shelf Habitat mapping and

ecology

Other techniques Mountjoy et al. (2009)

Preston et al. (2001)

Continental slope

Coastal to inner shelf

Geomorphology and

geohazards

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

Continental slope

Continental slope and volcanic islands

Continental slope

Mid-ocean ridge

Volcanic island

Continental slope

Coastal to inner shelf

Volcanic island

Continental slope

Geomorphology and

geohazards

Costa and Battista (2013)

Diesing et al. (2014)

Coastal

Coastal to inner shelf

Habitat mapping and

ecology

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).

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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

ecology

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.

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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

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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 – http://www.mareano.no/). 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-

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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

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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-

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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

geomorphometry

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

backgrounds.

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

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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

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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 www.hydrol-earth-syst-sci.net/20/3207/2016/

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,

1994).

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.

www.hydrol-earth-syst-sci.net/20/3207/2016/ 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 www.hydrol-earth-syst-sci.net/20/3207/2016/

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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Jan 2024

The purpose of the article is the development and research of a system for determining complex plans of spatial location and geometric characteristics using geodetic methods. The tasks set in the work are solved by analyzing the database of satellite geodetic and inertial measurement systems. Method. The application of satellite technologies, which allow determining the spatial position in automatic mode (shooting in motion), is a priority direction for solving the tasks of increasing the safety and efficiency of geodetic work in full in accordance with the strategic direction of scientific and technical development. The results. Since the 1950s, an intensive process of electronicization and automation has been observed in geodesy. Electronicization in geodesy made it possible to improve the technologies of performing geodetic works, to achieve higher productivity while increasing the accuracy of measurements. In principle, these processes and problems were internal to topographic and geodetic production and did not affect users directly. The first product that differed from traditional topographic maps and plans was digital terrain models. The first such model of the Earth's surface was created in 1957 in the USA at the Massachusetts Institute of Technology. It was a model of the relief of the earth's surface and was intended for the design of highways, constructed objects by optimizing project solutions. With the development of electronic computers and data processing methods, new forms of presenting information about the earth's surface began to appear. Currently, the study and research of the natural environment is closely related to the use of geographic information systems and various types of geo-information resources: electronic digital maps and spatial databases, GIS services and many others. Geo-informational software complexes allow you to process a large volume of data obtained during various studies, as well as organize and regulate information that has a spatial reference. Various geographic information systems, such as 3D cartography and virtual reality, are described, which allows creating more visual and understandable representations of geographic information. Geovisualization and 3D modeling provide quick access to geodata. Scientific novelty. The joint use of satellite and inertial measurements for surveying is substantiated, a theoretical solution for data processing is performed, and a methodology for drawing up large-scale plans is proposed. The geodetic component of the development of complex plans has been updated. Practical significance. The obtained results can be used to develop complex spatial development plans with a geodetic component. In addition, the materials of the article can be used in geodetic and cartographic activities.

Observation of radial velocity along the Yangtze River based on sentinel-1A

Article

Aug 2024

Bathymetric Mesh Simplification and Modelled Coastal Ocean Tides in New York Harbor

Preprint

Full-text available

Aug 2024

Accurate and timely predictions from operational forecast systems are crucial for disaster response planning during extreme weather events. Many of these forecast systems utilize unstructured mesh representations to model seafloor topography and discretize the domain. The size of the bathymetric mesh can significantly impact the runtime performance of these systems. Mesh simplification is a technique used to reduce the number of elements composing a mesh while preserving desired characteristics, such as shape and topology. Reducing the overall size of the mesh can improve the performance of any subsequent simulations performed on the mesh. In this work, vertex removal and re-triangulation operations are used to simplify a bathymetric surface model. Differences in resulting vertical offset from the original mesh and a maximum triangle area constraint are used to identify candidate vertices for elimination. Identical tidal simulations are modelled on the original and each simplified mesh. Velocity direction, velocity magnitudes, and water levels are recorded at twelve sites in New York Harbor over time. It was demonstrated that the simplified mesh derived from using even the strictest parameters for the mesh simplification was able to reduce the overall mesh size by approximately 26.81%, which resulted in a 26.38% speed improvement percentage compared to the un-simplified mesh. Reduction of the overall mesh size was dependent on the parameters for simplification and the speed improvement percentage was relative to the number of resulting elements composing the simplified mesh.

Multibeam Echosounder

Chapter

Jul 2024

Daniele Casalbore

A multibeam echosounder is an active sonar system which relies on high-frequency (tens/hundreds of kHz) sound waves to map the seafloor. The three most commonly used outputs are bathymetry, seafloor backscatter, and water column data. Since its advent, this technology has exponentially increased our knowledge of the processes that sculpt the seafloor, revealing a large spectrum of geomorphic features associated with oceanographic, volcano-tectonic, sedimentary, and gravity-driven instability processes. Characterization of such features is used for the assessment of marine geohazards, while repeated bathymetric surveys are used to monitor rapid seafloor changes related to hazardous phenomena such as coastal erosion, landslides, active tectonics, and volcanism. Repeat bathymetric surveys have thus become a complementary tool to on-land remote sensing techniques. This chapter briefly illustrates (1) the architecture of multibeam systems, (2) the main techniques used for data processing, and (3) the uncertainties associated with bathymetric data and computation of depth changes and involved volumes. Finally, an example of repeated multibeam systems used to detect and monitor a complex eruption-driven sequence of submarine/subaerial instability processes at Stromboli volcano is presented.

MODERN TECHNIQUES FOR IMPROVED TOPO/BATHY ELEVATION MODELING OF TIDAL INLETS

Conference Paper

Full-text available

Mar 2011

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.

GIS-based Analysis of Coastal Lidar Time-Series

Book

Full-text available

Jan 2014

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.

TASSE (Terrain Attribute Selection for Spatial Ecology) Toolbox v. 1.1

Code

Full-text available

Mar 2017

Vincent Lecours

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)

TASSE (Terrain Attribute Selection for Spatial Ecology) Toolbox v. 1.0

Data

Full-text available

Oct 2015

Vincent Lecours

Cartographic Relief Presentation

Book

Dec 1982

Eduard Imhof

Scale in Spatial Information and Analysis

Book

Apr 2014

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.

Channel and sandstone body geometry from 3D seismic and well control in Widuri field, offshore SE Sumatra, Indonesia

Conference Paper

Oct 1998

D.C. Carter

Geomorphometric Parameters: A Review and Evaluation

Article

Oct 1975

David M. Mark

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

Book

Jul 2016

Igor Florinsky

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.

The seabed appearance of different coral bank provinces in the Porcupine Seabight, NE Atlantic: results from sidescan sonar and ROV seabed mapping

Conference Paper