445
1 INTRODUCTION
Ashorelineisaboundarybetweenthelandandwater
surfaces [1]. It is characterised by instability and
functional diversity which vary depending on the
region[2].Thisboundaryisofparticularimportance
fromtheperspectiveofeconomicandenvironmental
policiesofthecoastalstates.Thisstemsfrom
thefact
thattheshorelineisrichinnaturalresources,whichis
why approx. 50% of theworld’s populationinhabits
theareas locatedwithin 100 km of the shoreline [1].
Therefore, it is essential to monitor the state of the
seashore,whichchangesrapidlyandisdeterminedby
numerous anthropogenic
and natural factors. These
include: biological activity, coastal flooding [3],
earthquakes [4], marine erosion, ocean acidification
[5],oceancurrents,riverregulation,sealevelrise[6],
seawater intrusion [7], temperature increase, tides,
transportation of the rock debris [8] or wave action.
Research into the impact of the above‐mentioned
factors on the
shoreline course is conducted in a
variety of waterbodies such as bays [9], river deltas
Shoreline Extraction Based on LiDAR Data Obtained
Using an USV
A.Halicki
1,2
,M.Specht
1,3
,A.Stateczny
4
,C.Specht
3
&O.Lewicka
3
1
MarineTechnologyLtd.,Gdynia,Poland
2
UniversityofPorto,Porto,Portugal
3
GdyniaMaritimeUniversity,Gdynia,Poland
4
GdańskUniversityofTechnology,Gdańsk,Poland
ABSTRACT: This articleexplores the use of Light Detection And Ranging (LiDAR) derived point clouds to
extracttheshorelineoftheLakeKłodno(Poland),basedontheirgeometryproperties.Thedatacollectionwas
performed using the Velodyne VLP‐16 laser scanner,
which was mounted on the HydroDron Unmanned
Surface Vehicle (USV). A modified version of the shoreline extraction method proposed by Xu et al. was
employed, comprising of the following steps: (1) classifying the point cloud using the Euclidean cluster
extraction with a tolerance parameter of 1 m and min. cluster size
of 10,000 points, (2) further filtration of
boundarypointsbyremovingthosewithheightabove1mfromthemeasuredelevationofwatersurface,(3)
manualdeterminationofacurveconsistingof5pointslocatedalongtheentireshorelineextractionregionata
relativelyconstantdistantfromthecoast,(4)
removalofpointsthatarefurtherfromthecurvethantheaverage
distance,repeatedtwice.ThemethodwastestedonthescannedsectionofthelakeshorelineforwhichGround
ControlPoints(GCP)weremeasuredusingaGlobalNavigationSatelliteSystem(GNSS)RealTimeKinematic
(RTK)receiver.Then,theresultswere
comparedtothegroundtruthdata,obtaininganaveragepositionerror
of2.12mwithastandarddeviationof1.11m.Themaxerrorwas5.54m,whilethemin.errorwas0.41m,all
calculated on 281 extracted shoreline points. Despite the limitations of this parametric, semi‐supervised
approach,
thosepreliminaryresultsdemonstratethepotentialforaccurateshorelineextractionbasedonLiDAR
data obtained using an USV. Further testing and optimisation of this method for larger scale and better
generalisation for different waterbodies are necessary to fully assess its effectiveness and feasibility. In this
context, it is essential to develop
computationally efficient methods for approximating shorelines that can
accuratelydeterminetheircoursebasedonasetofpoints.
http://www.transnav.eu
the International Journal
on Marine Navigation
and Safety of Sea Transportation
Volume 17
Number 2
June 2023
DOI:10.12716/1001.17.02.22
446
and estuaries [10,11], wetlands [12], as well as other
geographicformationssituatedalongthecoast[13,14].
Due to complex shoreline dynamics, different
indicators[e.g. High Water Line (HWL), MeanHigh
WaterLine(MHWL)]areusedtodefineanddescribe
shoreline [1]. Moreover, different authors often use
divergentdefinitionsforthe
sameshorelineindicators
[1].
Similarly,manydifferentmethodsfordetermining
the shoreline course are applied throughout the
literature.These include: geodeticsurveys[15,16], in
particular,thoseusingtheGlobalPositioningSystem
(GPS) and remote sensing measurements [17]
performed using unmanned and manned airborne
systems[18],aswellassatellites[19].
Inrecentyears,
LightDetectionAndRanging(LiDAR)hasbecomea
popular method for shoreline determination. LiDAR
measurementsaretypicallyperformedusingairborne
systems[13],andallowforalargeareatobecovered
inarelativelyshorttime[20,21].Themethodinvolves
emittingabeamoflightataspecific
wavelengthand
recordingthereturnsignal if thebeamencounters a
reflector(i.e.,alightreflectingobject).By measuring
the time, it takes for the beam to return and taking
intoaccountthedeviceʹsorientationinspaceandthe
angleatwhichthebeamwasemitted,itispossible
to
calculate the position of the reflector. LiDAR has
severaladvantagesoverothershorelinedetermination
methods, including its ability to capture detailed
topographic information, its high accuracy and
precision,aswellasitsabilitytoprovidedatainreal‐
time [22]. However, the use of LiDAR also has its
limitations,including
relativelyhighcost,theneedfor
extensivedatapre‐processing,aswellasdependence
onenvironmentalandweatherconditions,whichcan
makeLiDARlesspracticalforsomeapplications.
Farris et al. [22] compared three shoreline
extraction methods used by the United States
Geological Survey (USGS) as part of the Marine
Geology Program. The first of them is a modified
profile method as described by [21]. It uses a 20 m‐
wide window determined along the transverse
profiles.Thesecondoneisthegridmethodbasedon
theinterpolationofheightsontoagridofsquares.The
thirdoneisa contour
methodthatallowsacontourof
theMeanHighWater(MHW)leveltobeobtainedby
usingthecontourgenerationfunction in the ArcGIS
software.Aspartofthevalidationtests,avisualand
quantitative assessment of the shoreline extraction
accuracywasconductedbasedontheAirborneLaser
Scanning (ALS)
data recorded using the ATM‐II
system. The measurements were performed on Fire
Island (USA) by the National Oceanic and
AtmosphericAdministration(NOAA) and the USGS
intheyears2000and2012.Theauthorshadnodata
on the actual position of the shoreline, which
prevented the comparison of the errors
in the
determination of its course. For this reason, they
decided to compare the differences in the extraction
resultsbetweentheindividualmethods.Theauthors
quantitatively demonstrated that the shoreline
courses obtained using the contour, grid and profile
methods are very similar to each other, with shifts
betweenthemof
lessthan1m.
Fernández Luque et al. [23] developed the
Elevation Gradient Trend Propagation (EGTP)
method for shoreline extraction, which uses the
iterative method based on a grid of squares. The
EGTP method involves the use of the elevation
gradient trend (its size and direction) calculated for
eachgridcell
ofaknownelevationtowardscellsofan
unknownelevation.Thisprocessisrepeateduntilthe
newpointofthegridreachesalevelsimilarto(lower
than) the selected vertical reference system. In this
way,itiseasytodeterminetheshorelinecoursefrom
the extrapolated terrain model. As
part of the
validationtests,avisualandquantitativeassessment
of the shoreline extraction accuracy was conducted
based on the ALS data recorded using the Leica
Geosystems ALS60 system. The measurements were
performed along the Mediterranean coast in the
Almeria province (Spain) in 2009. The shoreline
extraction errors were referred to
62 control points
that were determined using a Differential Global
PositioningSystem(DGPS)receiver.Asdemonstrated
by statistical analyses,themean uncertaintyand the
medianuncertaintyfortheEGTPmethodwere2.08m
and 1.51 m, respectively. The study results obtained
usingthe elevationgradient trend propagationwere
compared with
the results obtained using the
referencemethodsasproposedby[21,22].TheEGTP
methodhasbeenproventohaveahigheraccuracyof
the shoreline course determination than that of the
referencemethods.
Hua et al. [24] developeda method for detecting
shoresofananthropogenicnature. At the beginning
of
thepaper,attentionwasdrawntothelargevolume
of data derived from LiDAR measurements.
Therefore, the authors proposed simple criteria to
limitthesizeoftheLiDARpointcloud,thusreducing
thecomputationalcomplexityatthelaterstagesofthe
anthropogenic method. External software was used
forthevisualisation
andanalysisoftheLiDARpoints.
This enabled the determination of the coordinate
range of the area under study, the coordinate range
within which the shoreline is found, the scanning
directionwhen using aircraft and the side on which
the shoreline was located on the scan. The program
also enabled the
performance of preliminary
segmentation(classification)oftheareaunderstudy.
Subsequently,thepointsthatmayhavebeenreflected
from the water surface were removed. Only then
couldtheshorelinecoursebedeterminedbasedonthe
informationon thedirectionof flight.As part ofthe
validation tests, a visual assessment
of the shoreline
extractionaccuracywasconductedbasedontheALS
data. The measurements were conducted in the
coastal zone of Longkou (China). The authors
compared the method they had proposed with the
contour method only visually. Unfortunately, they
failedtodescribethereferencemethod.
Liu et al. [25] proposed two
shoreline extraction
methods,bothofthemusingLiDARdataandremote
sensing imagery. It is noteworthy that the authors
created and made available a plugin for the ArcGIS
software named “ShorelineExtractor”, which enables
the determination of the shoreline course using the
contour and object‐oriented methods. The contour
method subtracts
the elevation of the local tidal
system from the elevation in the Digital Terrain
Model (DTM). In this way, a contour (a shoreline)
with an elevation of 0 m is obtained. On the other
hand,intheobject‐orientedmethod,aclusterofland
or water pixels is regarded as an
object, while the
447
shorelinesarecreatedasboundariesbetweenclusters
of different classes. The “ShorelineExtractor”
extension also enables the generalisation and
smoothingoftheshorelineobtainedusingoneofthe
two methods proposed by the authors. The first of
them is the shoreline simplification by the Douglas‐
Peucker method, which preserves points that
are
relevantintermsofmaintainingthebasicshapeofthe
curve.Thesecondmethodinvolvesananalysisofthe
shoreline shape and the elimination of bends with
highcurvature.Aspartofthevalidationtests,avisual
assessment of the shoreline extraction accuracy was
conductedbasedontheALS
datarecordedusingthe
ATM system. The measurements were conducted in
thecoastalzoneofGalvestonBay(USA)in1999.The
studydemonstratedthattheaccuracyoftheshoreline
coursedeterminationwas4.5m(p=0.95).Itshouldbe
pointed out that the use of a constant tidal datum
value for a
large region could lead to an error in
shorelinepositiondetermination.
Xu et al. [26] proposed a parametric method of
shoreline extraction based on the cloud of points
surveyed by LiDAR. The first part of the algorithm
involves the detection and rejection of the points
belongingtothewatersurface
byusingplanefitting
by the RANdom SAmple Consensus (RANSAC)
method [29], as well as density and distance
characteristics of individual points [30]. The second
part of the algorithm involves classification of the
land returns using the Euclidean cluster extraction
[27,28]. The indication of potential boundary points
and the optimisation of
the boundary formed from
them based on the cost function optimisation model
[26,31]. As part of the validation tests, a visual and
quantitative assessment of the shoreline extraction
accuracywasconductedbasedontheALSdata.The
measurements were carried out on five waterbodies
withdifferentgeometricalandopticalcharacteristics:
Bowman Lake, Canyon Stream, Oregon Estuary,
SusquehannaRiverandWaxLake,intheyears2005–
2014. Shoreline extraction accuracy metrics, such as
correctness and completeness, were calculated as
90.7% and 92.5%, respectively. Moreover, it was
demonstrated that the accuracy of the shoreline
course determination by the parametric method on
fivedifferent
waterbodieswas1m[26].Theobtained
results were compared with the results presented in
fourdifferentpapersaddressingsimilarissues.Ithas
been proven that their accuracy level was 1.5–31 m,
i.e.lowerthanthatoftheparametricmethod[26,32–
35].Itshouldbenotedthattheextractionresultswere
obtained on various data sets (aerial images and
LiDARpoints)withdifferentspatialresolutions.The
authors also addressed the issues related to the
parametricityofthemethodproposed.Theoperation
of the algorithm was tested for different parameter
values. In their article, they provided suggested
valuesofindividualparametersforwhich
satisfactory
resultsoftheshorelineextractiononfivewaterbodies
wereobtained.
Yousef et al. [36,37] developed a morphological
shoreline extraction method, which uses a DTM
created based on the ALS data and the local tidal
system.Themorphologicalalgorithmcompriseseight
main stages. The first stage is the process of
converting the point cloud from LiDAR
measurementsintotheformofadigitalterrainmodel.
In the second stage, thesegmentation (classification)
ofeachcelloftheDTMtooneofthetwoclasses:land
or water, is performed. In the third stage, the
anomalies that are interpreted as outliers and
measurementerrorsaredetectedandremovedusing
the neighbourhood test. In the fourth stage,
constrainedmorphologicalopenandcloseoperations
are carried out in order to remove the remaining
artifacts,suchasgapsbetweentheneighbouringland
areasorbrokenpartsofwaterareas.Inthefifthstage,
smallisolated
landandwaterbodiesareremoved.In
thesixthstage,theHoughtransform[38]isappliedto
removestructuresofananthropogenicnature,suchas
bridges, docks or fishingpiers. In the seventh stage,
the shoreline is determined and subsequently
smoothed. To this end, the authors performed the
Gaussian kernel. In
the eighth stage, the shoreline
obtained was superimposed on an aerial image in
ordertovisuallyassesstheextractionresults.Aspart
of the validation tests, a visual and quantitative
assessment of the shoreline extraction accuracy was
conductedbasedontheALSdatarecordedusingthe
LMS‐Q680i system. The
measurements were
performed along the coast of the USA, passing
through three states: New Jersey, Rhode Island and
Virginia,intheyears2008–2012.Inordertoassessthe
shoreline extraction accuracy, the shoreline
determined manually based on an aerial image was
used. Moreover, a Monte Carlo simulation was
performed in the
article in order to estimate the
shoreline extraction errors using the morphological
method. Statistical analyses showed that the mean
errorandthestandarddeviationwere1.21mand1.97
m,respectively.Thestudyresultsobtainedusingthe
morphological method were compared with the
results obtained using the reference methods
proposed
by [25,39]. The morphological method has
been proven to have a higher accuracy of shoreline
position determination than that of the reference
methods.
As part of the INNOBAT project [40], it was
decidedtoimplementtheparametricmethod[26]for
shoreline extraction. An important advantage of the
methodis theuse
of onlythegeometricalproperties
of the LiDAR point cloud. The method proposed
enablesthe fullautomationof the extractionprocess
and offers the possibility for conducting further
research to attempt to develop specific parameter
valuesforaparticularmeasurement(waterbodytype,
the nature of the shoreline and measurement
conditions)
[31]. Moreover, according to the results
presented by the original authors, the parametric
method enables the fulfilment of the accuracy
requirements provided for the most rigorous
International Hydrographic Organization (IHO)
order,i.e.theExclusiveOrder(horizontalaccuracyof
5m(p=0.95))[41],whichrefertotheworksrelatedto
the shoreline
course determination. In view of the
above, the aim of this article is to validate the
parametric method of shoreline extraction based on
theLiDARdatarecordedusinganUnmannedSurface
Vehicle(USV).
448
2 MATERIALSANDMETHODS
2.1 Datacollection
The shoreline extraction was performed on data
collected with the Velodyne VLP‐16 laser scanner
mounted on the HydroDron USV. Obtained results
were validated against the ground‐truth data
determinedwithaGlobalNavigationSatelliteSystem
(GNSS) Real Time Kinematic (RTK) receiver. The
studyareawasashorelinesectionoftheLakeKłodno
(Poland).Boththelakeandthemeasurementareaare
presentedonFigure1.
Figure1.SatelliteimageoftheLakeKłodnowiththeareain
which the hydrographic surveys were conducted marked
witharedrectangle.
The data was collected and georeferenced to the
PL‐UniversalTransverseMercator(UTM)(zone34N)
and PL‐EVRF2007‐NH systems using the HYPACK
software. To compensate for the movements of the
vessel and obtain accurate positions, the HYPACK
program was integrated with the Ekinox2‐U Inertial
NavigationSystem(INS)anda
GNSSRTKreceiver.
2.2 Shorelineextractionmethod
Theshorelineextractionmethodusedinthisworkis
basedonthemethodproposedbyXuetal.[26].The
modifications involve additional filtration steps and
skipping of the shoreline approximation using the
cost model proposed by the authors of the original
method.
This was due to the problems with the
implementationoftheoriginalapproach,describedin
furthersectionsofthearticle.Theshorelineextraction
method used in this work comprises of five main
stages:
1. ClassificationofpointsintheLiDARderivedpoint
cloud into clusters using Euclidean clustering,
describedindetail
byRusu[28];
2. Indication of potential boundary points using the
authors’originaltestalgorithm[26];
3. Filtration of potential boundary points using the
elevationthreshold;
4. Manual indication of curve points in the water
along the entire shoreline section at a relatively
constantdistancefromtheshore;
5. Calculation
of the average distance between the
points and the formed curve, rejection of points
foundfurtherthantheaveragedistance.
2.3 Extractionerrorcalculation
In order to quantitatively measure the error of the
extraction method, the Euclidean distance was
computed between coordinates of each extracted
point and the coordinates of the
closest Ground
Control Point (GCP). The Mean Error (ME) was
calculated using the Euclidean distance, and its
formulaisgivenbyEquation1:

1
1
min ,
N
ij
j
i
M
EdPGCP
N
, (1)
where:
N–numberofextractedshorelinepoints(–);
i–numberingrepresentingshorelinepoints(–);
j–numberingrepresentingGCPs(–);
P
i–i‐thshorelinepoint(–);
GCP
j–j‐thGCP(–);
d(P
i,GCPj)–Euclideandistancebetweeni‐thshoreline
pointandj‐thGCP(m).
The Euclidean distance for a pair of shoreline
pointsp,qisgivenbyEquation2:



222
,
pq pq pq
dpq x x y y z z , (2)
where:
d(p,q) – Euclidean distance between points p, q in
three‐ dimensionalCartesiancoordinatespace(m);
p, q – numbering representing a pair of shoreline
points(–);
x–longitudeoftheshorelinepoint(m);
y–latitudeoftheshorelinepoint(m);
z–heightoftheshorelinepoint(m).
Min.andmaxvaluesoftheshorelinepositionerror
were calculated in similar manner, as well as the
standarddeviationofthemean.Obtainedvalues
are
presentedattheendoftheResultssection.
3
RESULTS
The shoreline extraction method applied was
developedbased onthe extraction methodproposed
byXuetal.[26].Severalissuesthatweredescribedin
detail in this article prevented the authors of this
study from implementing the method described in
[26] in its original form. Additional filtering steps
were required
in order to remove the excess points.
Moreover, the method for shoreline approximation
givenasetofboundarypointswasnotperformeddue
to problems with its implementation. Instead, the
manualconnectionofextractedpointswasperformed
for ease of visual analysis. Additionally, the mean
shoreline position error and deviation
were
calculated.Itshouldbealsonotedthatthepointcloud
datausedinthisstudywasobtainedusingadifferent
technique [Terrestrial Laser Scanning (TLS) from a
movingUSVratherthanALS].Belowaretheresults
oftheextractionroutineperformedinthispaper.The
following steps assume that the
cloud does not
contain many water returns. Otherwise, prior
filtration of water returns should be performed, e.g.
usingthedensityanddistancethresholdsmentioned