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Remote sensing techniques are currently the main methods providing elevationdata used to produce Digital Terrain Models (DTM). Terrain attributes (e.g. slope,orientation, rugosity) derived from DTMs are commonly used as surrogates of spe-cies or habitat distribution in ecological studies. While DTMs’ errors are known topropagate to terrain attributes, their impact on ecological analyses is howeverrarely documented. This study assessed the impact of data acquisition artefacts onhabitat maps and species distribution models. DTMs of German Bank (off NovaScotia, Canada) at five different spatial scales were altered to artificially introducedifferent levels of common data acquisition artefacts. These data were used in 615unsupervised classifications to map potential habitat types based on biophysicalcharacteristics of the area, and in 615 supervised classifications (MaxEnt) to predictsea scallop distribution across the area. Differences between maps and models builtfrom altered data and reference maps and models were assessed. Roll artefactsdecreased map accuracy (up to 14% lower) and artificially increased models’ per-formances. Impacts from other types of artefacts were not consistent, eitherdecreasing or increasing accuracy and performance measures. The spatial distribu-tion of habitats and spatial predictions of sea scallop distributions were alwaysaffected by data quality (i.e. artefacts), spatial scale of the data, and the selection ofvariables used in the classifications. This research demonstrates the importance ofthese three factors in building a study design, and highlights the need for errorquantification protocols that can assist when maps and models are used in deci-sion-making, for instance in conservation and management.
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Influence of artefacts in marine digital terrain models on
habitat maps and species distribution models: a multiscale
Vincent Lecours
, Rodolphe Devillers
, Evan N. Edinger
, Craig J. Brown
Vanessa L. Lucieer
Fisheries & Aquatic Sciences, School of Forest Resources & Conservation, University of Florida, 7922 NW 71st Street, Gainesville 32653, Florida
Department of Geography, Marine Geomatics Research Lab, Memorial University of Newfoundland, 232 Elizabeth Avenue, St. John’s,
Newfoundland and Labrador, Canada A1B 3X9
Department of Geography, Marine Habitat Mapping Research Group, Memorial University of Newfoundland, 232 Elizabeth Avenue, St. John’s,
Newfoundland and Labrador, Canada A1B 3X9
Applied Research, Nova Scotia Community College, 80 Mawiomi Place, Dartmouth, Nova Scotia, Canada B2Y 0A5
Institute for Marine and Antarctic Studies, University of Tasmania, 20 Castray Esplanade, Battery Point, Tasmania 7004, Australia
Artefacts, error propagation, habitat
mapping, multibeam bathymetry, species
distribution model, terrain analysis
Vincent Lecours, Fisheries & Aquatic Sciences,
School of Forest Resources & Conservation,
University of Florida, Gainesville, FL 32653.
Tel: +1 352 273 3617; Fax: +1 352 392
3672; E-mail:
Funding Information
This project was funded by the Natural Sciences
and Engineering Research Council of Canada
(NSERC) and the Canadian Foundation for
Innovation (CFI). Memorial University Libraries
provided funding through their Open Access
Author Fund.
Editor: Duccio Rocchini
Associate Editor: Mat Disney
Received: 13 March 2017; Revised: 15 May
2017; Accepted: 21 May 2107
doi: 10.1002/rse2.49
Remote sensing techniques are currently the main methods providing elevation
data used to produce Digital Terrain Models (DTM). Terrain attributes (e.g. slope,
orientation, rugosity) derived from DTMs are commonly used as surrogates of spe-
cies or habitat distribution in ecological studies. While DTMs’ errors are known to
propagate to terrain attributes, their impact on ecological analyses is however
rarely documented. This study assessed the impact of data acquisition artefacts on
habitat maps and species distribution models. DTMs of German Bank (off Nova
Scotia, Canada) at five different spatial scales were altered to artificially introduce
different levels of common data acquisition artefacts. These data were used in 615
unsupervised classifications to map potential habitat types based on biophysical
characteristics of the area, and in 615 supervised classifications (MaxEnt) to predict
sea scallop distribution across the area. Differences between maps and models built
from altered data and reference maps and models were assessed. Roll artefacts
decreased map accuracy (up to 14% lower) and artificially increased models’ per-
formances. Impacts from other types of artefacts were not consistent, either
decreasing or increasing accuracy and performance measures. The spatial distribu-
tion of habitats and spatial predictions of sea scallop distributions were always
affected by data quality (i.e. artefacts), spatial scale of the data, and the selection of
variables used in the classifications. This research demonstrates the importance of
these three factors in building a study design, and highlights the need for error
quantification protocols that can assist when maps and models are used in deci-
sion-making, for instance in conservation and management.
In the last decades, remote sensing has become the main
method used for collecting elevation data used in the pro-
duction of Digital Terrain Models (DTM). All DTMs
carry a certain level of error (Gessler et al. 2009) caused
by random noise, systematic errors and artefacts (Wise
2000). Artefacts were characterized by Reuter et al.
(2009)) as “distinct erratic features” that are made of
improbable and incorrect values. Artefacts can be found
in DTMs collected from any remote sensing systems
(Fisher and Tate 2006; Sofia et al. 2013) and at all scales.
ª2017 The Authors. Remote Sensing in Ecology and Conservation published by John Wiley & Sons Ltd on behalf of Zoological Society of London
This is an open access article under the terms of the Creative Commons Attribution-NonCommercial License, which permits use,
distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.
Artefacts can be introduced by the interpolation method
used to create the DTM (Sofia et al. 2013), the motion
and location of the acquisition platform (Harrison et al.
2009), timing or log frequency issue in the surveying sys-
tem (Lecours and Devillers 2015) or a lack of or an inap-
propriate correction of ionospheric and atmospheric
conditions (Li and Goldstein 1990). Artefacts can be
problematic as they influence data quality more than
other types of errors like noise and imprecise measure-
ments (Rousseaux 2003) and can be very subtle in the
DTM (Filin 2003), making them “the most significant
errors in a spatial or statistical analysis because they are
not easily detected yet introduce significant bias” (Brown
and Bara 1994).
DTMs are now commonly used in Geographic Informa-
tion Systems (GIS) to derive terrain attributes (e.g. slope,
orientation, rugosity) that can be used as surrogates for
other phenomena in fields like ecology (Bolstad et al. 1998)
and biogeography (Franklin 2013). Artefacts in DTMs were
shown to sometimes propagate to the derived terrain attri-
butes (Sofia et al. 2013; Lecours et al. 2017a) and are likely
to impact subsequent analyses (Arbia et al. 1998; Heuvelink
1998). Mapping and quantifying error propagation
throughout analysis have received some attention in the
geospatial literature (e.g. Fisher and Tate 2006; Wilson
2012) but are rarely performed by DTM users from other
disciplines (van Niel and Austin 2007). Quantifying error
propagation from DTM is especially relevant for the pro-
duction of species distribution models (SDM) and habitat
maps (van Niel et al. 2004; Peters et al. 2009) that often
combine terrain attributes with other environmental data
(Franklin 1995; Guisan and Zimmermann 2000; Williams
et al. 2012; Leempoel et al. 2015). These maps and models
are regularly used to support decision-making in conserva-
tion (Miller 2010; Guisan et al. 2013). However, a lack of
understanding of errors, their propagation and spatial dis-
tribution in maps may result in inaccurate maps and mod-
els that could lead to inappropriate decisions (Beale and
Lennon 2012), and negative impacts on biodiversity or
stakeholders (Beven 2000; Regan et al. 2005; Etnoyer and
Morgan 2007). However, issues related to spatial data error
are often overlooked (but see van Niel et al. 2004 and Livne
and Svoray 2011). To our knowledge, the influence of data
acquisition artefact errors in DTMs has never been assessed
on maps resulting from SDM or habitat mapping exercises.
The objective of this study was to describe the impact
of some common remotely sensed data acquisition arte-
facts on marine habitat maps and SDMs. Our specific
objectives were to (1) quantify the impact of artefacts on
habitat maps accuracy and SDMs performance, to (2)
assess if impacts are dependent on spatial scale, and to
(3) assess if impacts can be attenuated when combining
the affected data with other environmental data of better
quality. Our hypotheses were that artefacts do negatively
affect habitat maps and SDMs, that the impacts are
greater at finer scales, and that the addition of relatively
better quality data reduces the impacts of artefacts on
maps and models.
Material and Methods
Case study and data
This article explored the impact of DTM artefacts on
habitat maps using a case study from the marine environ-
ment. The marine realm provides an ideal case as it has
been suggested that underwater DTMs, or Digital Bathy-
metric Models (DBM), may be more prone to errors and
artefacts than terrestrial DTMs (Hughes-Clarke et al.
1996; Passalacqua et al. 2015; Lecours et al. 2016a).
DBMs are often the only available datasets used to char-
acterize deep-water environments due to difficulties to
observe and sample other environmental characteristics
(Solan et al. 2003; Robinson et al. 2011). If multibeam
echosounders (MBES) are currently the best technology
enabling the collection of large DBMs (Kenny et al.
2003), most bathymetric surfaces generated from these
systems still contain some artefacts (Hughes-Clarke
2003a; Roman and Singh 2006). Since these artefacts are
often within hydrographic error standards (Hughes-
Clarke 2003a) and appear even when appropriate calibra-
tion and corrections are made (Erikstad et al. 2013), they
are often considered inherent to the data and tend to be
overlooked by DBM end-users.
This article used bathymetric data for German Bank,
off Nova Scotia (Canada), in the eastern Gulf of Maine
(Fig. 1). The surveyed area covers 3650 km
of the Sco-
tian Shelf and has been extensively studied in previous
works (e.g. DFO, 2006; Brown et al. 2012; Todd et al.
2012; Smith et al. 2017). Bathymetric data were collected
by the Canadian Hydrographic Service (CHS) and were
corrected in post-processing for tide, motion, and sound
velocity. The corrected soundings were used to generate
reference DBMs at five different spatial resolutions: 10 m,
25 m, 50 m, 75 m and 100 m in the bathymetric process-
ing software CARIS HIPS and SIPS v.9.0. These five refer-
ence DBMs were assumed to be free of artefacts, and
following methods described in Lecours et al. (2017a), 10
different amplitudes of heave, pitch, roll and time arte-
facts were artificially introduced in them by altering the
calibration measures of the different surveys (Table 1).
The ten levels of amplitude for each type of artefacts were
derived from the standard deviation (r; Table 1) of the
ship’s recorded range of motion at the time of surveys.
As described in Lecours et al. (2017a), these common
artefacts were selected based on their different theoretical
2ª2017 The Authors. Remote Sensing in Ecology and Conservation published by John Wiley & Sons Ltd on behalf of Zoological Society of London
Impacts of Artefacts in Habitat Mapping V. Lecours et al.
impact on data; pitch impacts bathymetric data in both
horizontal and vertical planes, heave impacts them in a
vertical plane, roll affects soundings that are further away
from the nadir in a vertical plane consequently affecting
areas that overlap between different survey lines and
time causes a relative shift of adjacent lines in the hori-
zontal plane (see Hughes-Clarke 1997, 2002, 2003a,b; Lur-
ton 2010). Similar artefacts can also be found in other
types of remote sensing like LiDAR (Brown and Bara
1994; Filin 2003; Lichti and Skaloud 2010). The artefacts
were systematically introduced to provide controlled
conditions that enable comparisons of results, as often
performed in evaluations of the impact of error on
analyses (Reuter et al. 2009).
Six terrain attributes that together summarize topo-
graphic variability were derived from the reference and
altered DBMs using the TASSE toolbox for ArcGIS
(Lecours 2015): slope, easterness and northerness, topo-
graphic mean, rugosity and topographic position (see
Lecours et al. 2016b, 2017b). Backscatter data (i.e. acous-
tic reflectance) were simultaneously recorded with the
bathymetric data. The backscatter data were processed
and transformed by Brown et al. (2012) into three deriva-
tive layers that inform on seafloor properties (e.g. surficial
geology, porosity): Q1, Q2 and Q3. Finally, two sets of
ground-truth data from Brown et al. (2012) were used:
(1) 3190 geo-referenced photographs of the seafloor clas-
sified into five habitat types (reef, glacial till, silt and
mud, silt with sediment bed forms, sand with sediment
bed forms and highly abundant sand dollars (Echinarach-
nius parma)), and (2) 4816 geo-referenced sea scallop
observations (Placopecten magellanicus). Details on how
these data were collected and processed and examples of
photographs of the seafloor can be found in DFO (2006)
and Brown et al. (2012).
Habitat Maps and SDMs
Using the 205 sets of bathymetric and terrain attribute sur-
faces (i.e. one set for each of the 10 levels of artefacts, for
the four types of artefacts, at five different resolutions, in
addition to a reference set for each of the five resolutions),
habitat maps and SDMs were produced for three scenarios.
First, maps and models were generated using only the
bathymetry and the six terrain attribute surfaces, thus
Figure 1. Digital bathymetric model of the
German Bank study area.
Table 1. Levels of artefacts introduced in the five reference DBMs.
Standard deviations (r) were derived from the recorded motion at
time of survey.
Level of induced artefact
Heave (m) 0.33 0.66 0.99 1.32 1.65
Pitch (°)1.65 3.30 4.95 6.60 8.25
Roll (°)1.01 2.02 3.03 4.04 5.05
Time (sec) 0.25 0.50 0.75 1.00 1.25
A positive pitch indicates that the bow is up and a positive roll means
that the port side is up.
ª2017 The Authors. Remote Sensing in Ecology and Conservation published by John Wiley & Sons Ltd on behalf of Zoological Society of London 3
V. Lecours et al. Impacts of Artefacts in Habitat Mapping
accounting only for terrain morphology (i.e. hereafter
referred to as “7 layers” scenario). Then, maps and models
were produced using all the available data (bathymetry,
slope, easterness, northerness, topographic mean, rugosity,
topographic position, Q1, Q2 and Q3) (i.e. “10 layers” sce-
nario). The addition of backscatter data in this scenario
was done to address our third hypothesis regarding the
addition of relatively better quality data to the mapping
process. Finally, maps and models were built using only
non-correlated variables (i.e. “8 layers” scenario). On Ger-
man Bank, the steepest areas are also the ones with the
highest rugosity, resulting in a high correlation between the
slope and rugosity data layers. Also, bathymetry is highly
correlated with topographic mean as they are closely
related (see Lecours et al. 2017b). Rugosity and topo-
graphic mean were therefore not used for the last sets of
maps and models. The data for the eight layers scenario
were thus bathymetry, slope, easterness, northerness, topo-
graphic position, Q1, Q2 and Q3. Overall, 615 habitat
maps and 615 SDMs were produced and analysed by using
the 205 sets of data for each of the three scenarios with
both approaches.
The method used to generate habitat maps is based on
the concept of benthoscape (Zajac 2008), a representation
of the biophysical characteristics of an area generated by
adopting a landscape style approach similar to when maps
of landscape features are generated from terrestrial data-
sets. Such approach was used by Brown et al. (2012) to
map features on the seafloor that could be resolved
within the acoustic remotely sensed data, thus not
attempting to delineate seafloor attributes beyond what
the remote sensing techniques were capable of resolving.
This approach segments the different data layers into a
statistically optimum number of classes that are then spa-
tially compared to the geo-referenced photographs and
recombined based on best match with the different habi-
tat types (see Brown et al. 2012). The Modified k-Means
unsupervised classification tool of Whitebox GAT v.3.2
was used to produce these maps, and confusion matrices
were built to calculate the kappa coefficient of agreement
of each map (Boyce et al. 2002).
SDMs were generated based on maximum entropy
(MaxEnt), a common and effective method (Phillips et al.
2006; Monk et al. 2010), that used the sea scallop obser-
vations to segment the environmental data and quantify
sea scallop habitat suitability across the area. SDMs were
computed using the MaxEnt software v.3.3.3k with the
same settings as in Brown et al. (2012). Area under the
curve (AUC) derived from threshold independent receiver
operating curves were also measured to quantify the per-
formance of the models and enable comparisons (Phillips
et al. 2006): AUC
was measured to evaluate the good-
ness-of-fit of models to the training data, and AUC
was used to evaluate the ability of models to perform well
on an independent dataset (i.e. validation samples) (Fitz-
patrick et al. 2013). These two measures were combined
to compare models’ performance, robustness and general-
izability (Vaughan and Ormerod 2005; Warren and Seifert
2011). AUC
the difference between AUC
was used to quantify generalizability (i.e.
transportability, transferability): a high value is an indica-
tion that a model over-fitted the training data and does
not replicate well to a different dataset. Details on these
measures can be found in Lecours et al. (2016b). Finally,
correlations between model outputs were calculated to
evaluate spatial similarity of predictions.
Habitat maps
The average kappa coefficients of agreement of all maps
produced with altered data and their standard deviation
are presented in the supporting information (Table S1),
while the individual kappa of the 615 habitat maps are
presented in Figure 2. In general, habitat maps produced
using 10 layers provided the best classifications, followed
by those using eight layers and those built from only seven
layers. However, map accuracy varied less for the scenario
with eight layers (i.e. it was more consistent). Heave was
generally the artefact type that made map accuracy vary
the least, while roll artefacts usually made map accuracy
vary the most. In average (Table S1), only three sets of
maps showed a scale-dependent pattern for which maps
produced from finer-scale data were more impacted by
artefacts than maps made from broader-scale data. These
sets all belong to the scenario with 10 layers and were
maps impacted by pitch, roll and time. A visual assessment
of the results showed that the presence of artefacts in data
used to produce habitat maps has a noticeable influence
on the spatial distribution of the habitats (cf. Fig. 3).
When matching the total area misclassified because of
artefactsthat is, when comparing the classifications made
from altered data to one made from reference datato the
difference in kappa coefficient between these maps, results
show that a little difference in kappa coefficient can trans-
late into large differences in spatial output (cf. Fig. 3C).
Except for maps affected by roll artefacts, the reference
maps did not always produce the best outcome in terms
of accuracy: while all habitat maps impacted by any level
of roll artefact performed worse than the reference maps,
an important number of maps made from altered data
had a higher kappa coefficient than their corresponding
reference maps. This was observed regardless of scale and
scenario. Overall, 47% of the habitat maps altered by
pitch had a higher kappa coefficient than their
4ª2017 The Authors. Remote Sensing in Ecology and Conservation published by John Wiley & Sons Ltd on behalf of Zoological Society of London
Impacts of Artefacts in Habitat Mapping V. Lecours et al.
corresponding reference map, and that percentage was
higher for time (50%) and heave (55%). No particular
scale-dependent patterns were observed, except for habitat
maps made from seven layers and impacted by pitch, for
which a greater amount of maps performed better than
the reference maps at broader scales.
–2 –1.5 1 –0.5 0 0.5 1 1.5 2
Level of heave (m)
Level of time (s)
Level of pitch (°)
Level of roll (°)
–2 –1.5 –1 –0.5 0 0.5 1 1.5 2
–2 –1.5 –1 –0.5 0 0.5 1 1.5 2
–9–8–7–6–5–4–3–2–1 0 1 2 3 4 5 6 7 8 9
–9–8–7–6–5–4–3–2–1 0 1 2 3 4 5 6 7 8 9
–6 –5 –4 –3 –2 –1 0 1 2 3 4 5 6
–6 –5 –4 –3 –2 –1 0 1 2 3 4 5 6
–1.5 –0.5 0.5 1.5
–1.5 –0.5 0.5 1.5
–2 –1.5 –1 –0.5 0 0.5 1 1.5 2
–9–8–7–6–5–4–3–2–1 0 1 2 3 4 5 6 7 8 9
–6 –5 –4 –3 –2 –1 0 1 2 3 4 5 6
–1.5 –0.5 0.5 1.5
7 layers 8 layers 10 layers
7 layers 8 layers 10 layers
7 layers 8 layers 10 layers
7 layers 8 layers 10 layers
Kappa coefficient (%) Kappa coefficient (%) Kappa coefficient (%) Kappa coefficient (%)
10 m 25 m 50 m 75 m 100 m
Figure 2. Kappa coefficients of agreement of the 615 habitat maps.
ª2017 The Authors. Remote Sensing in Ecology and Conservation published by John Wiley & Sons Ltd on behalf of Zoological Society of London 5
V. Lecours et al. Impacts of Artefacts in Habitat Mapping
Figure 3. Examples of habitat maps produced with eight layers at 50 m resolution, overlaid by the ground-truth data. The colour of the ground-
truth data matches the classification’s colour when appropriately classified. (A) shows the reference map that was built with data that were
assumed free of artefacts. (B) shows maps built from data that were impacted by different types of artefacts. (C) shows the spatial distribution of
the change in habitat map classification between the maps from (B) and the map from (A). Red pixels indicate change while grey pixels are those
that were classified as the same habitat type in the two compared classifications.
6ª2017 The Authors. Remote Sensing in Ecology and Conservation published by John Wiley & Sons Ltd on behalf of Zoological Society of London
Impacts of Artefacts in Habitat Mapping V. Lecours et al.
Species distribution models
Figure 4 shows how the SDMs’ performance and robust-
ness change as artefacts are introduced in the input DBMs
for the eight layers scenario. For all types of artefacts, no
pattern could be observed regarding whether some scales
were more impacted than others, or whether a greater
level of artefact resulted in higher or lower performance
or robustness.
In general, results show that introducing heave artefacts
decreased models’ performance and tend to also decrease
models’ robustness. One major exception was observed to
these patterns: at 75 m resolution, 7 and 10 layers models
performed better than the reference models. The two ref-
erence models in these cases had a higher standard devia-
tion and a lower AUC
. Overall, about 87% of models
impacted by heave had a lower performance index than
their comparable reference model. About 39% of models
with pitch artefacts had a higher AUC
than their
respective reference model while 36% of them were more
robust, which resulted in 41% of these models with a
higher performance index than their reference model
(Fig. 4). Roll artefacts boosted model performance, as
87% of models built from altered data had higher
measures than the reference models. However,
only 26% of the models impacted by roll artefacts were
more robust than the reference models. The combination
of these two metrics into the performance index indicated
that 56% of models impacted by artefacts had a higher
index than the reference models. In terms of time arte-
facts, 29% of the models that were built from altered data
performed better than the reference models and 11% were
more robust, resulting in 26% of them having a higher
performance index than reference models.
In terms of generalizability, it was difficult to find any
consistent general patterns except for those models
affected by roll: 95% of them showed a greater generaliz-
ability index than the reference models. On the other
end, the presence of heave artefacts decreased the general-
izability in 80% of the cases, compared to 53% of models
affected by pitch and 66% of those affected by time
(Fig. 5).
Regarding spatial outputs, the presence of artefacts
always introduced discrepancies in the distribution of rel-
ative habitat suitability (cf. Figs. 6 and S1). While the
average discrepancies could be globally small (e.g. 2.2%),
they could be locally important (e.g. 58.9%). It is also
interesting to note that a high measure of correlation
between a model impacted by artefacts and its reference
model did not necessarily involve high similarity between
those. For instance, a correlation coefficient of 0.963 still
resulted in 23% of the area for which differences in rela-
tive habitat suitability were >5% (Fig. 6).
In general, models accounting only for topography and
depth (seven layers) were consistently the most affected
by artefacts in comparison to the reference models
10 m 25 m 50 m 75 m 100 m
Performance index
Level of heave (m)
–2 –1.5 –1 –0.5 0 0.5 1 1.5 2
–9 –8 –7 –6 –5 –4 –3 –2 –1 0 1 2 3 4 5 6 7 8 9
–6 –5 –4 –3 –2 –1 0 1 2 3 4 5 6
–1.5 –1 –0.5 0 0.5 1 1.5
Level of roll (°)
Level of pitch (°)
Level of time (s)
Figure 4. Change in performance index (ratio of AUC
on standard deviation) as the level of artefacts in the data changes, for the models
built from eight layers. Models that perform better have a high AUC
and models that are more robust have a low standard deviation. High-
performance models are often less robust than less performing models: the performance index thus captures the trade-off between performance
and robustness, with higher values of performance index indicating a better trade-off.
ª2017 The Authors. Remote Sensing in Ecology and Conservation published by John Wiley & Sons Ltd on behalf of Zoological Society of London 7
V. Lecours et al. Impacts of Artefacts in Habitat Mapping
(Fig. S1), while those built with uncorrelated variables
(eight layers) were often the least impacted. The ranges in
correlation coefficients (Fig. S1) were usually not very big
for heave artefacts and were in average lower for roll arte-
facts. Overall, roll seemed to have the most impact on the
spatial distribution of relative habitat suitability of sea
scallop. No clear pattern was observed in terms of scale,
although roll artefacts seemed to produce models that
were more similar to the reference ones at coarser scales,
and the extreme scales (10 and 100 m) seemed to be a bit
more impacted by heave, time and pitch than the inter-
mediate scales (2575 m).
Impacts of artefacts on habitat maps and
Our first hypothesis was that artefacts in bathymetry
that propagate to terrain attributes would impact habi-
tat maps and SDMs in a negative way. Results show
that this is not always the case. While we were expect-
ing map accuracy to decrease as a function of level of
artefacts, only maps impacted by roll demonstrated such
relationship. Results show that the other types of arte-
facts sometimes artificially increased map accuracy,
although not in a predictable way. A higher level of
artefact did not necessarily result in a better or worse
map or model than a lower level of artefact. About half
of the habitat maps produced with data altered by
heave, pitch and time artefacts performed better than
the reference maps. While these results suggest a ran-
dom pattern, they may also have been influenced by
the approach used to quantify map accuracy. Since the
habitats are represented on maps as clusters of pixels
showing similar characteristics, they share some charac-
teristics with areal data. Artefacts might thus influence
the boundaries of these “zones” more than the area
inside them. Because the ground-truth data are points
that are more likely to fall within the middle of a zone
than at its boundary, the kappa coefficients of agree-
ment may not capture the change in boundary. A spa-
tial assessment of the differences between the different
habitat maps, as performed for instance in Figure 3C
and in Diesing et al. (2014), could help better capture
the influence of artefacts on the delineation of the dif-
ferent habitat zones. Considering the amount of maps
produced in this study, this would be computationally
intensive but such an approach should be considered in
future work. These results however yield an important
conclusion regarding the methods commonly used in
the literature to quantify classification and habitat map
accuracy: measures using point data to validate classifi-
cations of zones may be biased by not capturing the
variability of the classifications along zone boundaries.
The analysis of SDMs yielded similar conclusions to the
analysis of habitat maps but from different types of arte-
facts. Heave artefacts had generally a negative impact on
10 m 25 m 50 m 75 m 100 m
Generalizability index
Level of heave (m)
Level of roll (°)
Level of pitch (°)
Level of time (s)
–6 –5 –4 –3 –2 –1 0 1 2 3 4 5 6
–9 –8 –7 –6 –5 –4 –3 –2 –1 0 1 2 3 4 5 6 7 8 9
–1.5 –1 –0.5 0 0.5 1 1.5
–2 –1.5 –1 –0.5 0 0.5 1 1.5 2
Figure 5. Change in generalizability index (ratio of AUC
on AUC
) as the level of artefacts in the data changes, for the models built from
eight layers. Higher values indicate more generalizable or replicable models.
8ª2017 The Authors. Remote Sensing in Ecology and Conservation published by John Wiley & Sons Ltd on behalf of Zoological Society of London
Impacts of Artefacts in Habitat Mapping V. Lecours et al.
the performance of models, with some exceptions (e.g.
75 m resolution modelswhich may indicate that this par-
ticular scale does not capture the relevant drivers of species
distribution). Some models impacted by pitch and time
performed better than the reference models. Models
impacted by roll artefacts clearly contradicted our hypothe-
sis: the performance of most of these models was artifi-
cially increased by the presence of roll artefacts. This could
be explained by the fact that sea scallops distribution is
driven by rugosity (Brown et al. 2012), and artefacts like
roll and pitch artificially increase the rugosity of an area.
Models produced with data impacted by these artefacts
would thus artificially increase the relative habitat suitabil-
ity of sea scallops across the entire area, resulting in a
higher prediction success when validated against the test
data. The increase in high values of relative habitat suit-
ability was confirmed by visual comparison (cf. Fig. 6) but
also by the high differences in spatial correlation recorded
for pitch and particularly for roll (cf. Fig. S1).
Our second hypothesis stated that the impacts of artefacts
in bathymetry and terrain attributes should be greater at
finer scales. This hypothesis was based on the fact that the
propagation of artefacts from DTM to terrain attributes was
previously found to be scale-dependent (cf. Lecours et al.
2017a). Results from both unsupervised and supervised clas-
sifications did not confirm, neither did they refute, this
hypothesis as no particular scale-dependent patterns could
be identified. The difference between the scale-dependent
propagation of DTM artefacts in terrain attributes and the
scale-independent propagation of these artefacts in habitat
Habitat suitability
High : 1
Low : 0
Less than 5%
5% to 10%
10% to 15%
15% to 20%
20% to 25%
More than 25%
Heave Pitch
Roll Time
Minimum: 0.0%
Maximum: 58.9%
Mean: 2.2%
Standard deviation: 3.2%
Minimum: 0.0%
Maximum: 63.0%
Mean: 3.4%
Standard deviation: 4.1%
Minimum: 0.0%
Maximum: 74.6%
Mean: 8.6%
Standard deviation: 8.0%
Minimum: 0.0%
Maximum: 58.1%
Mean: 2.2%
Standard deviation: 3.2%
No artefact
Proportion of change >5%: 58%
Correlation with reference map: 0.832
Proportion of change >5%: 11%
Correlation with reference map: 0.980
Proportion of change >5%: 11%
Correlation with reference map: 0.980
Proportion of change >5%: 23%
Correlation with reference map: 0.963
Figure 6. Differences in distribution of relative habitat suitability of sea scallops between models affected by artefacts and a reference model
(top). The scenario represented is the one with eight layers at 50 m resolution. The level of error represented is the highest one (5r, Table 1).
ª2017 The Authors. Remote Sensing in Ecology and Conservation published by John Wiley & Sons Ltd on behalf of Zoological Society of London 9
V. Lecours et al. Impacts of Artefacts in Habitat Mapping
maps and SDMs may be explained by the integration of a
biological/ecological context. The presence of artefacts in
finer-scale data may not result in a poor habitat classification
if these data and the scales at which they were collected and
analysed do not have an ecological meaning or do not match
the ecological scale of the phenomenon being studied, thus
being unsuitable regardless of their quality.
Finally, our third hypothesis was that the addition of rela-
tively better quality data would reduce the impacts of arte-
facts on maps and models. Results suggest that this
hypothesis is true for the habitat maps, as maps built with
the relatively good quality backscatter data were generally
more accurate. It however remains unclear whether this
improvement was caused by the quality of the data or their
nature (i.e. backscatter), which in this particular case was
known to be ecologically relevant (Brown et al. 2012). The
latter option is the most likely, considering results from
Lecours et al. (2016b) that showed that maps and models of
the same area produced only with backscatter and depth
data performed very well. Further work is thus required to
validate or invalidate this hypothesis with more certainty. In
addition, results showed that the range in measures of accu-
racy was more stable when uncorrelated data were used,
which could be an indication that a better choice in input
variable has the potential to stabilize and attenuate the
impacts of artefacts.
Spatial errors in ecology: comparisons with
other studies
In terms of data quality, the ecological literature has been
oriented mostly towards measurement uncertainty, and
the work performed on errors has largely focused on the
positional accuracy of species observations (e.g. Moudry
a 2012). The impact of DTM artefacts has been
studied before in a geomorphology and geomorphometry
context (e.g. Bonin and Rousseaux 2005) but rarely in
ecology. Of note is the work by van Niel et al. (2004) and
van Niel and Austin (2007) that studied the effect of error
in DTM on terrain attributes and predictive vegetation
modelling. Despite different approaches and types of error
studied, these studies and the current one yielded similar
conclusions regarding the fact that errors do propagate
throughout analyses, and affect distribution models
although not in an easily predictable way. In another eco-
logical study that looked at uncertainty and error propa-
gation, Livne and Svoray (2011) identified the need to
focus on assessing the behaviour of ecological models to
spatial errors at different spatial resolutions. While this
was addressed in the current study, results did not indi-
cate any scale-dependent pattern.
In the marine environment, researchers are aware of
artefacts as they are often, although not always (e.g.
Lucieer et al. 2012), acknowledged (e.g. Blondel and
omez Sichi 2009). When acknowledged, their implica-
tions for the ecological analysis being performed are often
not discussed (e.g. Kostylev et al. 2001). The presence of
artefacts in MBES data sometimes prevents their use or
the use of their derived terrain attributes in ecological
applications (e.g. Clements et al. 2010). When such data
are still used, artefacts have been linked to habitat mis-
classifications (e.g. Micallef et al. 2012; Costa and Battista
2013), to noise in results from unsupervised classifications
(e.g. Galparsoro et al. 2015), and to difficulties associated
with identification of seabed features (e.g., Dolan and
Lucieer 2014), among other consequences. As part of
their seabed mapping guidelines, the Norwegian Hydro-
graphic Service indicated that “seabed features shall not
be camouflaged by artefacts and artefacts must not appear
as seabed features” (NHS, 2013), and that artefacts in the
processed bathymetry “shall be kept at an insignificant
level not disturbing the seabed image” (NHS, 2013).
However, no procedures are indicated to assist in making
decisions regarding how to deal with artefacts when they
cannot be removed. This overview of the literature reflects
the lack of understanding of how artefacts impact ecologi-
cal analyses and interpretations, and the lack of knowl-
edge on how to respond to the presence of artefacts. The
work by Zieger et al. (2009) is however noteworthy as
they used terrain attributes and seafloor classification to
identify artefacts in flat areas before correcting for the
misclassifications caused by artefacts. Such methods may
however be inefficient in more complex areas as the clas-
sifications may be unable to distinguish which bathymet-
ric patterns are artefacts and which are actual natural
While this study has focused on artefacts in multibeam
bathymetric data, backscatter data are also often impacted
by artefacts (e.g. Collier and Brown 2005; Che Hasan
et al. 2012). Like for bathymetric data, some of these arte-
facts can be removed in post-processing (e.g. De Falco
et al. 2010; Lamarche et al. 2011) but a complete removal
is not always achieved. Backscatter data with artefacts
have been widely used (e.g. Rattray et al. 2009; Roberts
et al. 2009) as they may still yield useful observations.
Other times however, they are judged unusable for the
mapping or modelling exercise (e.g. Holmes et al. 2008).
It has been recognized that there is a broad misunder-
standing of backscatter within the end user community
(Lurton and Lamarche 2015). In this study, backscatter
data were used to evaluate the impact of adding relatively
better quality data to poor quality data within the same
analysis. As done with bathymetry in this study, future
work should evaluate the impacts of artefacts in backscat-
ter data on habitat maps and SDMs. It is to be expected
that like for bathymetry and terrain attributes, artefacts in
10 ª2017 The Authors. Remote Sensing in Ecology and Conservation published by John Wiley & Sons Ltd on behalf of Zoological Society of London
Impacts of Artefacts in Habitat Mapping V. Lecours et al.
backscatter data will have a greater impact if sediment
properties are ecologically relevant to the species, area or
problem studied. For instance, Copeland et al. (2013)
noted that artefacts in the backscatter data resulted in an
apparent striping pattern in their habitat classifications.
Implications for ecological applications
The results of this study have critical implications for eco-
logical studies that use DTMs and their derived terrain
attributes in their applications, which is a common prac-
tice (Bouchet et al. 2015; Lecours et al. 2016a). The use
of environmental variables such as terrain attributes has
been shown to improve predictions accuracy in SDMs
(Dobrowski et al. 2008). However, this study showed that
when artefact errors are present in DTMs, there is a
trade-off between the improved prediction that would be
gained from including the DTM and its derived terrain
attributes and the risk to produce inaccurate predictions.
Results showed that such predictions are not necessarily
revealed as lower or absence of predictions, but can be
important inflation in predictions. For instance, artefacts
may alter the quantification of species-environment rela-
tionships by artificially increasing the importance of
rugosity in habitat characterization. When rugosity is
known to be a surrogate of a particular species distribu-
tion, this leads to an overestimation of the suitable habi-
tat for that species.
Studies that include any assessment of data quality are
rare (van Niel and Austin 2007). The current study high-
lighted the sensitivity of maps and models to the observa-
tional scale and spatial errors like artefacts. Many calls have
been made in the literature for the quantification of uncer-
tainty and error propagation throughout ecological analy-
ses (e.g. Guisan et al. 2006; Lecours et al. 2015), and tools
have been proposed to deal with uncertainty (e.g. the Data
Uncertainty Engine by Brown and Heuvelink 2007) but not
with errors. The ecological community that makes use of
GIS tools and remote sensing techniques is usually aware of
this need but such protocol are not yet implemented in any
workflow. As stated by Li et al. (2012): “there are user com-
munities who may be aware of spatial data quality issues
but may not have at their disposal techniques and tools for
data quality assurance.” Such tools, associated with proper
standards, protocols and metadata, are becoming crucial to
enable a proper incorporation of error modelling in the dif-
ferent applications workflow. This will eventually lead to
results and interpretation that are grounded on solid foun-
dations, and more informed decisions. While it is impossi-
ble to avoid error and uncertainty in ecological analyses, it
is also important that practitioners stop avoiding it. An
acknowledgement of errors like artefacts and a discussion
on their potential impact on analyses will increase the
chances to make more informed decisions when these data
and analyses are used in contexts like conservation plan-
DTMs and terrain attributes are now commonly used in
ecological studies. Despite an awareness of the presence of
errors like artefacts in these data, their quality is rarely
assessed, acknowledged or discussed. The goal of this
study was to develop evidence linking the presence of
artefacts in DTMs with the accuracy of analyses per-
formed in ecological applications. Results demonstrated
that artefacts do impact habitat maps and SDMs,
although not in a predictable way. Roll artefacts showed
the most predictable influence, decreasing the accuracy of
habitat maps and artificially increasing the performance
and generalizability of SDMs. Other types of artefacts
sometimes increased map accuracy and model perfor-
mance and generalizability or decreased them. These con-
clusions may however change if different data were used;
perhaps that with higher-resolution data (e.g. 0.52 m),
the relative magnitude of the artefacts would be more
important and produce a much larger and consistent
effect. Results showed that the importance of the impacts
of artefacts on ecological applications strongly depend on
whether or not the methods are grounded in ecological
relevance, particularly in terms of the choice of variables
and the spatial scale of the data. While the influence of
errors on an analysis depends on the type and require-
ments of the analysis (Friedl et al., 2001), results gained
in this study are transposable to other applications that
use remotely sensed data like LiDAR-derived DTMs and
encounter similar artefacts. This study also highlighted
requirements for error quantification tools to become
widely available to scientists and practitioners with a wide
range of background and expertise. This will improve
standards and protocols and lead to more quality-aware
decisions in contexts like conservation.
We thank Emma LeClerc for her insightful comments on
early drafts of this manuscript, and two anonymous
reviewers for their comments on the final version. We
also thank Jessica A. Sameoto, Dr. Stephen J. Smith and
the Canadian Hydrographic Service (Department of Fish-
eries and Oceans Canada), for sharing their data of Ger-
man Bank. This project was funded by the Natural
Sciences and Engineering Research Council of Canada
(NSERC) and the Canadian Foundation for Innovation
(CFI). Memorial University Libraries provided funding
through their “Open Access Author Fund”
ª2017 The Authors. Remote Sensing in Ecology and Conservation published by John Wiley & Sons Ltd on behalf of Zoological Society of London 11
V. Lecours et al. Impacts of Artefacts in Habitat Mapping
Data Accessibility
Data are available upon request from the Department of
Fisheries and Oceans, Canada, for researchers who meet
the criteria for access. Requests can be sent to
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Supporting Information
Additional supporting information may be found online
in the supporting information tab for this article.
Table S1. Mean and standard deviation of kappa coeffi-
cients of agreement of the 10 maps made from altered data
for each type of artefact, each scenario and each scale.
Figure S1. Spatial variation in predictions of sea scallops
distribution as quantified by the range in correlation coeffi-
cients between models built from altered data and the refer-
ence models.
ª2017 The Authors. Remote Sensing in Ecology and Conservation published by John Wiley & Sons Ltd on behalf of Zoological Society of London 15
V. Lecours et al. Impacts of Artefacts in Habitat Mapping
... The production of habitat maps is a key decision-support tool for conservation management and planning (Costello, 2009) but requires detailed information regarding the physical characteristics of the seafloor. Many studies have shown the utility in data from multibeam echosounders (MBES), including both bathymetry and backscatter, for producing accurate and useful habitat maps (Lecours et al., 2017;Lacharité et al., 2018;Fakiris et al., 2019). ...
... Using segmented polygons created from the bathymetric and backscatter surfaces to create segmented mosaics of ARA curves identifies spatial differences in seafloor properties that may be difficult to derive from the ARA curves alone. Image segmentation has the added benefit of reducing the impact of MBES backscatter artefacts on model predictions by smoothing outliers present in the mosaic within homogeneous objects (Lecours et al., 2017). ...
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Accurate maps of biological communities are essential for monitoring and managing marine protected areas but more information on the most effective methods for developing these maps is needed. In this study, we use Wilsons Promontory Marine National Park in southeast Australia as a case study to determine the best combination of variables and scales for producing accurate habitat maps across the site. Wilsons Promontory has full multibeam echosounder (MBES) coverage coupled with towed video, remotely operated underwater vehicle (ROV) and drop video observations. Our study used an image segmentation approach incorporating MBES backscatter angular response curve and bathymetry derivatives to identify benthic community types using a hierarchical habitat classification scheme. The angular response curve data were extracted from MBES data using two different methods: 1) angular range analysis (ARA) and 2) backscatter angular response (AR). Habitat distributions were predicted using a supervised Random Forest approach combining bathymetry, ARA, and AR derivatives. Variable importance metrics indicated that ARA derivatives, such as grain size, impedance and volume heterogeneity were more important to model performance than AR derivatives mean, skewness, and kurtosis. Additionally, this study investigated the impact of segmentation software settings when creating segmented surfaces and their impact on overall model accuracy. We found using fine scale segmentation resulted in the best model performance. These results indicate the importance of incorporating backscatter derivatives into biological habitat maps and the need to consider scale to increase the accuracy of the outputs to help improve the spatial management of marine environments.
... Dentre estas imperfeições, os artefatos podem ser a fonte de erro mais problemática, pois influenciam nos parâmetros derivados. Embora vários esforços de pós-processamento tenham sido realizados por agências espaciais e instituições de pesquisa a fim de diminuir os erros (desvio de uma medição de seu valor real -Wechsler, 2003), os dados de elevação globais contemporâneos não estão livres de artefatos (Lecours et al., 2017;Hirt, 2018 (Diogrande, 2013). ...
... Antonic; Dalibor; Renata, 2000; Lindsay e Creed, 2005;Wechsler, 2007; Pike et al., 2009;Lecours et al., 2017). O Modelo Digital de Elevação utilizado no presente trabalho foi gerado (interpolação) com dados de elevação obtidos pela técnica de fotogrametria na escala 1:10.000 ...
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A topografia da superfície terrestre é resultante de duas forças antagônicas, as endógenas e as exógenas. A atuação dessas duas forças resulta em uma grande variedade de formas de relevo com características distintas, por isso, a importância da classificação e mapeamento da morfologia de uma paisagem. Uma das abordagens é a ferramenta Geomorphons, que tem como produto a estratificação da paisagem em 10 elementos únicos, mas reconhecíveis: cume, crista, ombro, espora, encosta, concavidade, sopé, vale, depressão e plano. No presente trabalho foi investigado o efeito da resolução espacial de Modelos Digitais de Superfície (MDSs) e de Elevação (MDE) na classificação automatizada do relevo do município de Campo Grande, Mato Grosso do Sul, utilizando a ferramenta Geomorphons. Os Geomorphons foram derivados automaticamente dos MDSs e MDE, de diferentes fontes (radar e aerolevantamento) e resoluções (5, 12,5, 30, 90 m), no programa SAGAGIS. Os resultados obtidos mostram que a classificação automatizada das formas de relevo é sensível a escala de mapeamento e a resolução espacial dos MDSs e MDE. Em resoluções espaciais menores (12,5, 30, 90 m), as características do terreno são generalizadas, suavizadas ou não detectadas, o que limita a capacidade preditiva e acurácia dos fenótipos do relevo derivados. A base de maior resolução espacial (MDE 5 m) representou de forma mais realista a heterogeneidade do relevo do município de Campo Grande. Palavras-chave: Fenótipos de relevo; Mapeamento do relevo; Resolução espacial de MDS e MDE; Escala.
... MBES data can present many type of artefacts mostly caused by the limits of the instrument, the motions of the survey vessel (dynamic systematic errors), poor tidal or water sound velocity control causing vertical shifts and sound refraction. These artefacts are difficult to eliminate completely and a common obstacle in automated marine mapping (Lecours et al., 2017). Artefacts are recurrent in the extensive INFOMAR MBES bathymetry dataset, which is a combination of data from hundreds of different surveys with an array of vessels and survey operators, acquired with different (improving) instrumentation, in the space of about 25 years. ...
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In this study we applied for the first time Fully Convolutional Neural Networks (FCNNs) to a marine bathymetric dataset to derive morphological classes over the entire Irish continental shelf. FCNNs are a set of algorithms within Deep Learning that produce pixel-wise classifications in order to create semantically segmented maps. While they have been extensively utilised on imagery for ecological mapping, their application on elevation data is still limited, especially in the marine geomorphology realm. We employed a high-resolution bathymetric dataset to create a set of normalised derivatives commonly utilised in seabed morphology and habitat mapping that include three bathymetric position indexes (BPIs), the vector ruggedness measurement (VRM), the aspect functions and three types of hillshades. The class domains cover ten or twelve semantically distinct surface textures and submarine landforms present on the shelf, with our definitions aiming for simplicity, prevalence and distinctiveness. Sets of 50 or 100 labelled samples for each class were used to train several U-Net architectures with ResNet-50 and VGG-13 encoders. Our results show a maximum model precision of 0.84 and recall of 0.85, with some classes reaching as high as 0.99 in both. A simple majority (modal) voting combining the ten best models produced an excellent map with overall F1 score of 0.96 and class precisions and recalls superior to 0.87. For target classes exhibiting high recall (proportion of positives identified), models also show high precision (proportion of correct identifications) in predictions which confirms that the underlying class boundary has been learnt. Derivative choice plays an important part in the performance of the networks, with hillshades combined with bathymetry providing the best results and aspect functions and VRM leading to an overall deterioration of prediction accuracies. The results show that FCNNs can be successfully applied to the seabed for a morphological exploration of the dataset and as a baseline for more in-depth habitat mapping studies. For example, prediction of semantically distinct classes as “submarine dune” and “bedrock outcrop” can be precise and reliable. Nonetheless, at present state FCNNs are not suitable for tasks that require more refined geomorphological classifications, as for the recognition of detailed morphogenetic processes.
... Similarly, through the partnership with NGU and IMR, the Norwegian Mapping Authority (traditionally focussed on safety of navigation) gains insight into bathymetric data quality issues important for geological interpretation and use in habitat mapping, which may be irrelevant for hydrography. Examples could be data artefacts that lead to misleading terrain attributes (Lecours et al. 2017a(Lecours et al. , 2017b or overenthusiastic data cleaning, which obscures real morphological features in deeper waters. ...
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Geology is a core component of two major multidisciplinary seabed-mapping initiatives in Norway (MAREANO, Marine Base Maps for the Coastal Zone). Helped by Norway’s Nature Diversity Act, which acknowledges geological and landscape diversity alongside biodiversity, geological information has gained recognition nationally as part of an essential foundation for knowledge-based management, both in the coastal zone and offshore. Recently, international focus on the United Nations Sustainable Development Goals has led to the proposal of Essential Geodiversity Variables, a framework for geological (geodiversity) information, intended to stand alongside Essential Variables already defined for climate, biodiversity and oceans (limited to ocean physics, biochemistry, biology, and ecosystems). Here we examine to what extent map products from the Geological Survey of Norway generated under these multidisciplinary mapping initiatives fit within this framework of Essential Geodiversity Variables and how well it is suited to information on marine geodiversity. Although we conclude that the framework is generally a good fit for the marine-relevant Essential Geodiversity Variable classes (geology and geomorphology), we examine opportunities for further highlighting quantitative geodiversity information. We present preliminary examples of substrate diversity and morphological diversity and discuss our experience of geological mapping as part of multidisciplinary initiatives. We highlight many benefits, which far outweigh any perceived or real compromises of this approach in monetary, practical and scientific terms.
... DDM has the potential to be beneficial for many scientific applications, from geological studies to oceanography and biology [10,23,48]. Several aspects of marine geosciences-seafloor characterization, sedimentary studies, offshore engineering, etc.-require high-quality DBMs such as the DDM [18,27,35,49]. The metadata and documentation associated with the DDM aims to facilitate its discovery by researchers when searching for the bathymetry best fitting their specific purposes. ...
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Denmark’s Depth Model (DDM) is a Digital Bathymetric Model based on hundreds of bathymetric survey datasets and historical sources within the Danish Exclusive Economic Zone. The DDM represents the first publicly released model covering the Danish waters with a grid resolution of 50 m. When modern datasets are not available for a given area, historical sources are used, or, as the last resort, interpolation is applied. The model is generated by averaging depths values from validated sources, thus, not targeted for safety of navigation. The model is available by download from the Danish Geodata Agency website. DDM is also made available by means of Open Geospatial Consortium web services (i.e., Web Map Service). The original datasets—not distributed with the model—are described in the auxiliary layers to provide information about the bathymetric sources used during the compilation.
... The field of benthic habitat mapping is well recognized, and extensive overviews of various approaches, methodologies, and technologies can be found in Brown et al. (2011), Anderson et al. (2008, Diaz et al. (2004), Solan et al. (2003), and Kenny et al. (2003). The ecological objectives of habitat mapping are wide ranging and include characterizing baseline conditions (Smith et al. 2015;Oakley et al. 2012;Hewitt et al. 2004), investigating the relationship between biological species/communities and environmental parameters across various spatial scales (Lecours et al. 2015;De Leo et al. 2014;LaFrance et al. 2014;McArthur et al. 2010;Ierodiaconou et al. 2007;Zajac et al. 2000), and creating species or habitat prediction and modeling tools (Porskamp et al. 2018;Ierodiaconou et al. 2018Ierodiaconou et al. , 2011Lecours et al. 2017;Mitchell et al. 2017;Young et al. 2015;Valesini et al. 2010;Degraer et al. 2008). In addition to studying an area of interest, mapping efforts can focus on a specific habitat type, such as fish habitat (Malcolm et al. 2016;Kendall et al. ...
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In 2012, Hurricane Sandy created a new tidal inlet at Fire Island National Seashore (FIIS) in New York, USA, consequently altering environmental conditions within Great South Bay. This event presented a unique opportunity to establish new ecological baselines, assess resulting ecological change, and explore management implications. This study focuses on benthic mapping within the bayside of FIIS using acoustic, grab sample, and imagery data. Biotope (habitat) maps were developed describing relationships between macrofaunal communities and their environment. Additionally, biotopes were prioritized by “ecological value” based on user-defined criteria (presence of seagrass and potential for higher trophic level interactions) to guide management. While there are limited pre-Sandy data for comparison, findings from this study suggest the inlet has been a positive ecological influence on the nearby area. Dense concentrations of mature blue mussels (Mytilus edulis) documented near the inlet are considered ecologically beneficial and represent a post-Sandy distinction in ecosystem structure; M. edulis was last common when the inlet was previously open (early 1800s). The inlet is also likely responsible for seagrass expansion near the inlet but decline in other areas. This study advances the utility of the Coastal and Marine Ecological Classification Standard (CMECS) by including CMECS-defined data in analyses and expanding the definition of “dominance.” CMECS played a key role in developing map units, interpreting biotopes, and establishing statistically significant and ecologically meaningful biotic–abiotic relationships. This study also highlights the value and management applications of benthic mapping specific to FIIS and more broadly and advocates for similar studies elsewhere.
... When such data are used for high-resolution gridding, artifacts in the form of wrong morphological features, such as ripples or steps, can be created. The results, especially when derivatives (such as slope or rugosity) are calculated to classify the surveyed regions, will create incorrect data and subsequent interpretations [9][10][11]. An uninformed algorithm will not be able to differentiate between artifacts and true morphology. ...
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Ocean science and hydroacoustic seafloor mapping rely on accurate navigation underwater. By exploiting terrain information provided by a multibeam echosounder system, it is possible to significantly improve map quality. This article presents an algorithm capable of improving map quality and accuracy by aligning consecutive pings to tiles that are matched pairwise. A globally consistent solution is calculated from these matches. The proposed method has the potential to be used online in addition to other navigation solutions, but is mainly targeted for post processing. The algorithm was tested using different parameter settings on an AUV and a ship-based dataset. The ship-based dataset is publicly available as a benchmark. The original accurate navigation serving as a ground truth, alongside trajectories that include an artificial drift, are available. This allows quantitative comparisons between algorithms and parameter settings.
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A determinação dos canais de drenagem e de atributos do terreno é dependente da resolução espacial de Modelos Digitais de Superfície (MDS) e Elevação (MDE) bem como da fonte dos dados altimétricos utilizados. Neste trabalho foi analisada a sensibilidade geomorfométrica da drenagem e superfície do terreno em uma bacia hidrográfica urbana rural à resolução espacial e à fonte de MDS e MDE empregados. Foram utilizados cinco MDS obtidos via sensoriamento remoto orbital (Alos Palsar, Aster GDEM, SRTM 30 e 90, Topodata), sendo dois deles produtos de reamostragem (SRTM 90 e Topodata), com resoluções espaciais de 12,5, 30 e 90 m, e um MDE de 5 m, gerado com dados altimétricos obtidos via sensoriamento remoto aéreo. A rede de fluxo extraída do MDE 5 permitiu o delineamento mais preciso e acurado que a dos MDS para locais onde a área de contribuição era maior. Nas cabeceiras de drenagem houve incongruências para todas as bases topográficas o que é atribuído ao algoritmo de distribuição de fluxo utilizado (D8). Nos MDS de baixa resolução espacial (Alos Palsar, SRTM 30, 90, Topodata e Aster), as características topográficas não foram representadas de forma realista. Os atributos do terreno derivados do MDE de alta resolução espacial representaram de forma realista as características topográficas da bacia hidrográfica, incluindo formas de relevo antropogênicas.
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Ao longo da história os reservatórios têm desempenhado um papel fundamental no desenvolvimento econômico. No entanto, as barragens transformam a paisagem e criam riscos de impactos irreversíveis. As pesquisas sobre os impactos ambientais, sociais e econômicos de barragens estão concentradas nos grandes reservatórios, ainda que os pequenos reservatórios prevaleçam na paisagem. Na bacia hidrográfica do córrego Guariroba há um grande reservatório construído para abastecer a cidade de Campo Grande, os pequenos reservatórios ainda não foram mapeados nem seus impactos ambientais analisados. Diante disso, este trabalho teve como objetivo mapear os pequenos reservatórios na bacia hidrográfica e analisar os impactos ambientais associados, a fim de gerar informações que subsidiem o planejamento e gerenciamento das atividades antrópicas. O mapeamento foi realizado em ambiente do SIG a partir de imagens de satélite de alta resolução espacial disponíveis no “World Imagery” do ArcMap e imagens históricas do Google Earth. Para a validação foram derivados de imagens do satélite Sentinel-2A índices espectrais do período seco (2019) e chuvoso (2020). Foram mapeados 66 pequenos reservatórios criados pela construção de barragens de terra em cursos d’água naturais, localizados, predominantemente, nas cabeceiras de drenagem, e 119 reservatórios em áreas úmidas, principalmente, às ribeirinhas. Alguns dos reservatórios mapeados, não apresentavam água armazenada, tanto no período seco quanto no chuvoso. Nas cabeceiras de drenagem, a maior parte das nascentes estão degradadas, com solo exposto, desbarrancamento da taipa e ausência de vegetação florestal no entorno. Nos reservatórios em áreas úmidas ribeirinhas utilizados para dessedentação animal e piscicultura, muitos também se encontram em processo de degradação ambiental.
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Data acquisition artefacts are commonly found in multibeam bathymetric data, but their effects on mapping methodologies using geographic information system techniques have not been widely explored. Artefacts have been extensively studied in terrestrial settings, but their study in a marine context has currently been limited to engineering and surveying technology development in order to reduce their amplitude during data collection and postprocessing. Knowledge on how they propagate to further analyses like environmental characterization or terrain analysis is scant. The goal of this paper is to describe the contribution of different types of artefacts to marine terrain attributes at multiple scales. Using multibeam bathymetric data from German Bank, off Nova Scotia (Canada), digital bathymetric models (DBMs) were computed at five different spatial resolutions. Ten different amplitudes of heave, pitch, roll, and time artefacts were artificially introduced to generate altered DBMs. Then, six terrain attributes were derived from each of the reference and altered DBMs. Relationships between the amplitude of artefacts and the statistical and spatial distributions of: 1) altered bathymetry and terrain attributes surfaces and 2) errors caused by the artefacts were modeled. Spatial similarity between altered and reference surfaces was also assessed. Results indicate that most artefacts impact spatial similarity and that pitch and roll significantly impact the statistical distribution of DBMs and terrain attributes while time and heave artefacts have a more subtle impact. Results also confirm the relationship between spatial data quality and spatial scale, as finer-scale data were impacted by artefacts to a greater degree than broader-scale data.
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Toolbox for ArcGIS built from results of Lecours et al. (2017) and validated in Lecours et al. (2016). This toolbox generates six independent terrain attributes that together summarize topographic or bathymetric variability. References: Lecours, V., Devillers, R., Simms, A.E., Lucieer, V.L., and Brown, C.J. (2017) Towards a framework for terrain attribute selection in environmental studies. Environmental Modelling & Software, 89:19-30. Lecours, V., Brown, C.J., Devillers, R., Lucieer, V.L., and Edinger, E.N. (2016) Comparing selections of environmental variables for ecological studies: a focus on terrain attributes. PLoS ONE, 11:e0167128. TO CITE: v. 1.1: Lecours, V. (2017) Terrain Attribute Selection for Spatial Ecology (TASSE) v. 1.0: Lecours, V. (2015) Terrain Attribute Selection for Spatial Ecology (TASSE
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Selecting appropriate environmental variables is a key step in ecology. Terrain attributes (e.g. slope, rugosity) are routinely used as abiotic surrogates of species distribution and to produce habitat maps that can be used in decision-making for conservation or management. Selecting appropriate terrain attributes for ecological studies may be a challenging process that can lead users to select a subjective, potentially sub-optimal combination of attributes for their applications. The objective of this paper is to assess the impacts of subjectively selecting terrain attributes for ecological applications by comparing the performance of different combinations of terrain attributes in the production of habitat maps and species distribution models. Seven different selections of terrain attributes, alone or in combination with other environmental variables, were used to map benthic habitats of German Bank (off Nova Scotia, Canada). 29 maps of potential habitats based on unsupervised classifications of biophysical characteristics of German Bank were produced, and 29 species distribution models of sea scallops were generated using MaxEnt. The performances of the 58 maps were quantified and compared to evaluate the effectiveness of the various combinations of environmental variables. One of the combinations of terrain attributes–recommended in a related study and that includes a measure of relative position, slope, two measures of orientation , topographic mean and a measure of rugosity–yielded better results than the other selections for both methodologies, confirming that they together best describe terrain properties. Important differences in performance (up to 47% in accuracy measurement) and spatial outputs (up to 58% in spatial distribution of habitats) highlighted the importance of carefully selecting variables for ecological applications. This paper demonstrates that making a subjective choice of variables may reduce map accuracy and produce maps that do not adequately represent habitats and species distributions, thus having important implications when these maps are used for decision-making.
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Geomorphometry, the science of quantitative terrain characterization, has traditionally focused on the investigation of terrestrial landscapes. However, the dramatic increase in the availability of digital bathymetric data and the increasing ease by which geomorphometry can be investigated using geographic information systems (GISs) and spatial analysis software has prompted interest in employing geomorphometric techniques to investigate the marine environment. Over the last decade or so, a multitude of geomorphometric techniques (e.g. terrain attributes, feature extraction, automated classification) have been applied to characterize seabed terrain from the coastal zone to the deep sea. Geomorphometric techniques are, however, not as varied, nor as extensively applied, in marine as they are in terrestrial environments. This is at least partly due to difficulties associated with capturing, classifying, and validating terrain characteristics underwater. There is, nevertheless, much common ground between terrestrial and marine geomorphometry applications and it is important that, in developing marine geomorphometry, we learn from experiences in terrestrial studies. However, not all terrestrial solutions can be adopted by marine geomorphometric studies since the dynamic, four-dimensional (4-D) nature of the marine environment causes its own issues throughout the geomorphometry workflow. For instance, issues with underwater positioning, variations in sound velocity in the water column affecting acoustic-based mapping, and our inability to directly observe and measure depth and morphological features on the seafloor are all issues specific to the application of geomorphometry in the marine environment. Such issues fuel the need for a dedicated scientific effort in marine geomorphometry. This review aims to highlight the relatively recent growth of marine geomorphometry as a distinct discipline, and offers the first comprehensive overview of marine geomorphometry to date. We address all the five main steps of geomorphometry, from data collection to the application of terrain attributes and features. We focus on how these steps are relevant to marine geomorphometry and also highlight differences and similarities from terrestrial geomorphometry. We conclude with recommendations and reflections on the future of marine geomorphometry. To ensure that geomorphometry is used and developed to its full potential, there is a need to increase awareness of (1) marine geomorphometry amongst scientists already engaged in terrestrial geomorphometry, and of (2) geomorphometry as a science amongst marine scientists with a wide range of backgrounds and experiences.
Management for the major sea scallop (Placopecten magellanicus) fisheries in Canada is based on maximum sustainable yield (MSY) biomass and fishing mortality reference points applied to the whole stock, under the assumption that fishing mortality is uniformly distributed in space. However, scallop fishing vessels concentrate fishing in areas that consistently exhibit high densities resulting in a nonuniform spatial distribution of fishing effort. This study applies a spatial model for fishing effort derived from satellite vessel monitoring system data, scallop habitat suitability maps, and relative scallop density from a spatial stock assessment model to evaluate precautionary approach reference points in support of sustainable management. Target harvest rates were evaluated in terms of MSY for the higher habitat suitability areas. The results indicated that although MSY for the spatial model were similar to those when assuming a uniform distribution of effort, the biomass and catch rates over all areas were higher. The spatial model predicted that the MSY would be taken with less fishing effort, potentially lessening the benthic impacts from the scallop fishery.
Terrain attributes (e.g. slope, rugosity) derived from digital terrain models are commonly used in environmental studies. The increasing availability of GIS tools that generate those attributes can lead users to select a sub-optimal combination of terrain attributes for their applications. Our objectives were to identify sets of terrain attributes that best capture terrain properties and to assess how they vary with surface complexity. 230 tools from 11 software packages were used to derive terrain attributes from nine surfaces of different topographic complexity levels. Covariation and independence of terrain attributes were explored using three multivariate statistical methods. Distinct groups of correlated terrain attributes were identified, and their importance in describing a surface varied with surface complexity. Terrain attributes were highly covarying and sometimes ambiguously defined within software documentation. We found that a combination of six to seven particular terrain attributes always captures more than 70% of the topographic structure of surfaces.