791

1 INTRODUCTION

The integration of geospatial data recorded with

various devices and systems is aimed at transform

them to a uniform spatial reference system in order to

be able to analyse the measurement results obtained

[1–4]. The process of geospatial harmonisation is

particularly important in the coastal zone, because it is

one of the most dynamic regions on the Earth, due to

the fact that it is influenced by the atmosphere,

human activities and hydrosphere [5,6]. In addition,

the bathymetric monitoring in this zone also helps to

prevent negative effects on the aquatic environment

and humans [7,8].

Bathymetric and topographic measurements in the

coastal zone are carried out using hydroacoustic and

optoelectronic devices and systems [7,9].

Hydroacoustic methods use the phenomenon of

echolocation, which consists in sending a high-

frequency sound wave deep into the water and then

receiving the wave reflected from the bottom. The

most commonly used hydroacoustic devices and

systems are: a hydrometric station, an Inertial

Multi-sensor Integration of Hydroacoustic and

Optoelectronic Data Acquired from UAV and U

Vehicles on the Inland Waterbody

. Specht

1,2

Gdynia Maritime University, Gdynia, Poland

Marine Technology Ltd., Gdynia, Poland

ABSTRACT: Hydrographic and photogrammetric measurements in the coastal zone are performed using

hydroacoustic and optoelectronic methods, in particular with the use of Unmanned Aerial Vehicles (UAV) and

Unmanned Surface Vehicles (USV). It should be remembered that each of the devices registers data in a

different spatial reference system. Therefore, before starting the analysis of geospatial data, e.g. terrain relief, it

is necessary to carry out the process of their integration (harmonisation). The aim of this article is to present a

multi-sensor integration of hydroacoustic and optoelectronic data acquired from UAV and USV vehicles on the

inland waterbody. Bathymetric, Light Detection And Ranging (LiDAR) and photogrammetric measurements

were carried out on the Lake Kłodno (Poland) in 2022 using the DJI Phantom 4 RTK UAV and two unmanned

vessels: AutoDron, which was equipped with a Global Navigation Satellite System (GNSS) Real Time Kinematic

(RTK) receiver and a Single Beam Echo Sounder (SBES), as well as HydroDron, on which a GNSS/Inertial

Navigation System (INS) and a LiDAR sensor were mounted. The topo-bathymetric chart generated using the

Surfer software by the Inverse Distance to a Power (IDP) (p=1) method was developed. A Digital Terrain Model

(DTM) generated by the IDP method is characterised by high accuracy. The difference between the interpolated

value and the measurement value for the R68 measure is 0.055

m, while for the R95 measure, it has a value of

0.187 m. Research has shown that multi-sensor fusion of geospatial data ensures the possibility of performing

bathymetric, LiDAR and photogrammetric measurements in the coastal zone in accordance with the accuracy

requirements provided for the International Hydrographic Organization (IHO) Exclusive Order (horizontal

position error

≤ 1 m (p=0.95), vertical position error ≤ 0.15 m (p=0.95)).

http://www.transnav.eu

the

International Journal

on Marine Navigation

and Safety of Sea Transportation

Volume 17

Number 4

December 2023

DOI: 10.12716/1001.17.04.

792

Navigation System (INS), a MultiBeam Echo Sounder

(MBES), a positioning system (Differential Positioning

System (DGPS) or Real Time Kinematic (RTK)), a

Single Beam Echo Sounder (SBES), a SOund

Navigation And Ranging (SONAR) and a sound

velocity probe [10–15]. The operation of optoelectronic

methods consists in converting electrical signals into

optical signals and optical signals into electrical

signals. The most commonly used optoelectronic

devices and systems are: an Airborne Lidar

Bathymetry (ALB), an image sensor (a photodiode

detector, a photomultiplier tube or Charge-Coupled

Device (CCD) and Complementary Met-al-Oxide-

Semiconductor (CMOS) cameras), an INS, a laser

rangefinder, a positioning system (DGPS or RTK), a

RAdio Detection And Ranging (RADAR) and a

Terrestrial Laser Scanning (TLS) [16–21].

Liu et al. [22] proposed a Merge-Normalization

(MN) method that is suitable for the multi-sensor

fusion of bathymetric data in deep ocean waters. This

method will help solve the problem of the integration

of bathymetric data acquired from different sources in

deep waters in order to generate a high-precision

Digital Bathymetric Model (DBM). The study used the

data acquired using MBES (recorded by the Japan

Agency for Marine-Earth Science and Technology

(JAMSTEC) and the National Geological and

Geophysical Data Center (NGDC) since 2003) and

SBES echo sounders (recorded by NGDC since 2000),

Electronic Navigational Charts (ENC) (recorded by

the National Oceanic and Atmospheric

Administration (NOAA) since 2005), as well as Shuttle

Radar Topography Mission (SRTM) data sets. The

validation study was conducted in the deepest known

oceanic trench on the Earth, i.e. the Mariana Trench

(Pacific Ocean). The study demonstrated that the

multi-sensor fusion of ENC, MBES, SBES and SRTM

data enabled the creation of a high-precision digital

bathymetric model with a resolution of 100 m. The

DBM will enable the exploration of the subduction

zone and the seismological mechanism in the Trench

region. Moreover, the generated digital bathymetric

model was compared with the model generated by

the General Bathymetric Chart of the Oceans

(GEBCO) in 2014. The results show that the high-

resolution DBM obtained by the MN method

represents the topographical details of seabed shape

better than the GEBCO model.

Lubczonek et al. [23] proposed a method for

integrating data acquired using Unmanned Aerial

Vehicle (UAV) and Unmanned Surface Vehicle (USV).

The aim of the article was to develop a bathymetric

chart that includes depths all the way to the shoreline.

The study was conducted on the shallow water Lake

Dąbie (Poland) with an average depth of 2.61 m. The

registered geospatial data was subjected to the

process of their integration in order to create a Digital

Terrain Model (DTM) of the waterbody. Five methods

were applied for terrain modelling: Inverse Distance

to a Power (IDP), kriging, Natural Neighbour

Interpolation (NNI), radial basis function and

triangulation. The study demonstrated that the

accuracy of terrain modelling methods used was high

(Mean Error (ME) = 0.01 m, Root Mean Square Error

(RMSE) = 0.03 m). Therefore, it be concluded that the

data acquired using UAV and USV vehicles can be

applied for compiling navigational charts of shallow

(coastal) waterbodies, analysing the seabed shape in

the vicinity of hydrotechnical structures, or

archeological mapping.

Masetti et al. [24] proposed Denmark’s Depth

Model (DDM) in the form of a DBM developed based

on hundreds of bathymetric measurements performed

within the Danish Exclusive Economic Zone (EEZ).

This is the first such DBM with a resolution of 50 m to

cover the entire marine area of Denmark. The digital

bathymetric model was generated by averaging depth

values based on archival data, since it cannot be used

for navigation. The DBM contains 75.8% of depth data

that were interpolated. 18.1% of depth data were

surveyed

using a MBES, 5% of depth data were

acquired from historical hydrographic soundings and

1.1% of depth data were surveyed using a SBES. The

digital bathymetric model covers an area of 232,679

km2 of Danish waters, of which more than 97.5% of

data had depths of less than 100 m. The DDM can be

downloaded from the Danish Geodata Agency

website. The Denmark’s Depth Model is also made

available via the Open Geospatial Consortium Web

Map Service (WMS).

The literature research revealed that the

integration of geospatial data acquired by

hydroacoustic and optoelectronic methods for

topography modelling is commonly applied in the

coastal zone. Therefore, the aim of this article is to

present a multi-sensor integration of hydroacoustic

and optoelectronic data acquired using UAV and USV

vehicles on the inland waterbody. Its result will be a

three-dimensional DTM of the Lake Kłodno.

This article is structured as follows. Section 2

describes the measurement place (Lake Kłodno) and

the course of the performance of hydrographic and

photogrammetric measurements using UAV and USV

vehicles. Moreover, the section presents the method

for elaborating the data recorded during the study.

Section 3 presents a three-dimensional DTM in the

coastal zone. The paper concludes with final (general

and detailed) conclusions that summarise its content.

2 MATERIALS AND METHODS

2.1 Measurement place

Lake Kłodno, situated in the Kartuski District

(Pomorskie Voivodeship), was selected as the test

waterbody. This is an inland waterbody located in the

Kaszubski Landscape Park. The total area of the lake

is 134.9 ha, the length is 2.0 km and the max depth is

38.5 m. The study conducted by the Chief Inspectorate

of Environmental Protection in the years 2017–2018

[25] confirmed that this was a waterbody with a poor

water status (second purity class). The transparency of

water on this lake was measured using a Secchi disk

[26,27]. Research has shown that the transparency of

water amounted to 2.4 m [25]. The area under study

covered both the land and the water part of the

waterbody (Figure 1).

793

Figure 1. The location of topo-bathymetric measurements

conducted on the Lake Kłodno.

2.2 Realisation of bathymetric, LiDAR and

photogrammetric measurements

The measurement campaign on the Lake Kłodno was

conducted on 02-03 June 2022. The study was carried

out in four stages. As the first step, bathymetric

measurements of the waterbody were performed

using the AutoDron USV on which a GNSS RTK

receiver and a SBES were mounted [28–30]. The study

was conducted along 42 main sounding profiles,

which ran perpendicular to the shoreline course

direction and were located 10 m apart from each

other. They were designed in accordance with the

principles for performing hydrographic surveys

described in the International Hydrographic

Organization (IHO) S-44 standard [31]. Bathymetric

measurements were carried out under appropriate

hydrometeorological conditions, i.e. in windless

weather and at the water level of 0 in the Douglas

scale. A total of 7993 points were recorded in the PL-

Universal Transverse Mercator (UTM) (zone 34N) and

PL-EVRF2007-NH systems. In addition, when

performing bathymetric measurements, the water

level height was determined based on 21 points

surveyed by the geodetic method using a GNSS RTK

receiver.

In the second step, the laser scanning of the land

area adjacent to the shore was carried out using the

HydroDron USV on which a GNSS/INS system and a

Light Detection And Ranging (LiDAR) sensor were

mounted [32–34]. Hydrographic surveys were

conducted on 7 sounding profiles parallel to the shore

and located at a distance of 30-100 m from the

shoreline. For the recording and georeferencing of the

LiDAR point cloud, the HYPACK 2022 software was

used. The obtained point cloud was recorded in the

PL-UTM system (zone 34N) in the .las format. During

the recording, a min. distance threshold value approx.

equal to the length of the vehicle was used to reject its

own reflections from the HydroDron USV. Moreover,

for the georeferencing of the LiDAR point cloud, 141

characteristic points were used (shoreline course and

pier corners), determined by the geodetic method

using a GNSS RTK receiver.

The third stage of the study involved the

surveying of the land and water area of the

waterbody by the DJI Phantom 4 RTK UAV. Before

starting a flight pass, a photogrammetric control

network was designed and used for the

georeferencing of images taken by the drone. It

comprised 10 wooden markers (30 cm x 30 cm) that

were distributed uniformly over the area under study.

The geometrical centres of these markers were

surveyed using a GNSS RTK receiver. Following this,

the performance of photogrammetric measurements

started. They needed to be carried out under

appropriate meteorological conditions, i.e. no

precipitation, windless weather (wind speed not

exceeding 6–7 m/s) and a sunny day [35,36]. It was

decided to carry out a photogrammetric flight pass at

an altitude of 70 m. It was also adopted that the

gimbal angle would be 90°, while the longitudinal and

transverse coverage of images was set at 80%. During

the flight pass, 312 images were recorded.

The final stage of the study involved the

determination of waterbody depths in places where it

was not possible for the AutoDron USV to access.

These included depths between the shoreline and the

isobath of 0.6 m. The measurement was carried out by

the geodetic method, which involves a surveyor

entering water depths to a preset depth using a GNSS

receiver mounted on a pole [37–39]. The depth points

were located along the same 42 sounding profiles on

which the AutoDron USV moved during bathymetric

measurements. A total of 218 depth points were

recorded in the PL-UTM (zone 34N) and PL-

EVRF2007-NH systems [40].

2.3 Data elaboration

2.3.1 Bathymetric data elaboration

In the first step of the bathymetric data

elaboration, the depths recorded erroneously by the

SBES were deleted. It should be noted that in the

surveyed coastal zone of the Lake Kłodno, there are

many areas with ultra-shallow depths (less than 30

cm). In such areas, data is often recorded erroneously

as the hydroacoustic signal is repeatedly reflected

from the bottom, which necessitates the data cleaning

process to be carried out. After cleaning, the point

cloud contained 5297 depths. Figure 2 shows the

visualisation of the bathymetric data after the data

cleaning process. As can be noticed, the removed

depths are located close to the shoreline.

Figure 2. A view of the cleaned USV point cloud.

A draft of the echo sounder transducer (11 cm) was

then added to the cleaned depths. Moreover, the

depth values were referred to the target vertical

datum PL-EVRF2007-NH. These depths were not

794

referred to the chart datum, as no water level

recording was carried out on the Lake Kłodno.

The final stage of work when elaborating the

bathymetric data involved the transformation of the

plane coordinates recorded in the PL-2000 system into

the PL-UTM system. This follows from the data

integration assumptions, according to which all the

data must be recorded in the PL-UTM system.

Additionally, the depths between the shoreline

and the isobath of 0.6 m were attached to the

elaborated bathymetric data. In total, 255 recorded

points were noted. The data were provided through

the bathymetric measurements carried out by the

geodetic method and recorded in the PL-2000 plane

coordinate system and the PL-EVRF2007-NH normal

height system. Therefore, it was necessary to

transform the plane coordinates from the PL-2000

system to the PL-UTM system. The transformation

was carried out in the QGiS software. Certainly, the

geodetic points complemented the bathymetric data

recorded by the USV.

2.3.2 LiDAR data elaboration

Depending on the geodetic operations being

carried out in the area adjacent to the waterbody,

optoelectronic devices are used. As regards the

measurement campaign in Zawory, the device used

was the LiDAR mounted on the HydroDron USV. It

was used to record the LiDAR point cloud of the land

part along with the shoreline. However, these data

were only used to extract the shoreline.

The LiDAR point cloud was georeferenced in the

PL-UTM system, as the LiDAR device was integrated

with the Ekinox2-U INS on the HydroDron USV. The

recording and georeferencing of the LiDAR point

cloud were carried out using the HYPACK 2022

software.

The optoelectronic data in the .las format were

then uploaded to the CloudCompare and QGIS

softwares in order to extract the shoreline. It was

decided to extract the shoreline manually, i.e. by

drawing it based on the optoelectronic data (Figure 3).

Figure 3. A map showing the LiDAR data along with the

shoreline drawn as a point layer.

Initially, the shoreline was drawn as a linear layer.

However, for the purposes of data integration, it was

exported to the point layer. Moreover, all the points in

the layer were given a height value previously

surveyed by the GNSS receiver for the 0 m isobath in

the PL-EVRF2007-NH system. This value amounted to

160.385 m. The final stage of work involved exporting

the points to the .txt format. It was confirmed that the

bathymetric data surveyed by the geodetic method

could be used to determine the shoreline.

2.3.3 Photogrammetric data elaboration

Photogrammetric data elaboration is an important

rt of the work when developing a DTM in the

coastal zone, as these data cover both the land and

water part. These are particularly valuable in the

event that hydroacoustic devices fail to provide data

on shallow areas of up to 0.5 m.

The first stage of work when elaborating the

photogrammetric data involved importing the images

to the Pix4Dmapper software. Based on the

Exchangeable Image File Forma (EXIF) data contained

in the photos, the program selected a coordinate

system in which the images had been initially

recorded. The photos were recorded in the World

Geodetic System 1984 (WGS 84). Therefore, it was

possible to read the approximate location of the

images. The next stage of work was to georeference

the photos. Although the images had a coordinate

system assigned to them in order to increase their

accuracy, it was decided to carry out the previously

mentioned georeferencing. In the first step, a .csv file

with georeference point coordinates was loaded into

the Pix4Dmapper software. Moreover, two pairs of

coordinates were entered for a single georeference

point. Georeference points were then indicated on the

photos (Figure 4).

Figure 4. A view of the Pix4Dmapper software panel when

matching coordinates to images.

It should be noted that the plane coordinates of

georeference points were initially recorded in the PL-

2000 system, but were transformed to the PL-UTM

system for the DTM. The georeference point heights

were recorded in the PL-EVRF2007-NH system.

Having selected the relevant images in which the

georeference points were visible, the actual

georeferencing was started. It is worth mentioning

that the Pix4Dmapper software converts coordinates

to the target system using the seven parameter

transformation. It involves the transformation of

coordinates based on previously determined

parameters such as the scale factor, rotation matrices

and translation vectors. These parameters are

calculated through the relationships between the

points recorded in the original system (points

795

recorded on the phots) and the points recorded in the

target system (georeference points determined by the

GNSS RTK receiver) [41]. In order to assess the

accuracy, the differences were compared between the

coordinates of georeference points indicated on the

images and the coordinates of georeference points

surveyed by the GNSS RTK receiver (Table 1).

Table 1. The differences between the coordinates of

georeference points indicated on the images and the

coordinates of georeference points determined by the GNSS

RTK receiver.

________________________________________________

No. Point name d E

(m) dN

(m) dHn

(m)

________________________________________________

1 p_101 (3D) –0.006 –0.004 –0.003

2 p_3001 (3D) –0.008 0.018 0.020

3 p_3002 (3D) 0.001 –0.002 –0.054

4 p_3003 (3D) 0.034 –0.010 0.098

5 p_3004 (3D) 0.051 0.001 –0.158

6 p_3005 (3D) 0.002 –0.036 –0.030

7 p_3006 (3D) 0.041 –0.001 0.142

8 p_3007 (3D) 0.057 0.018 –0.098

9 p_3008 (3D) 0.013 –0.007 –0.001

10 p_3009 (3D) –0.027 0.005 –0.023

11 p_3010 (3D) 0.016 0.009 0.046

________________________________________________

RMS 0.030 0.014 0.080

________________________________________________

The differences between the easting

, northing

and normal height

coordinates of georeference

points indicated on the images and the coordinates of

georeference points determined by the GNSS RTK

receiver.

Based on Table 1, it should be stated that the

images taken by the UAV have a high degree of

accuracy. The Root Mean Square (RMS) of the

differences between the coordinates of georeference

points indicated on the images and the coordinates of

georeference points determined by the GNSS RTK

receiver were 0.030 m (easting), 0.014 m (northing)

and 0.080 m (normal height).

The next stage of work consisted of the automatic

data processing in the software. The UAV point cloud

was generated in the form of the GRID Digital Surface

Model with a grid spacing of 1 m in the formats of

.las, .laz and .xyz.

The final stage of the photogrammetric data

elaboration involved point cloud cleaning, which is a

mandatory process when developing a DTM. A

digital terrain model is created using points located

on the land surface, and it does not include land cover

features such as, e.g. buildings, trees and vegetation.

Therefore, it was necessary to carry out the

classification of the point cloud into the features of

buildings, infrastructure and vegetation. The

automatic classification process was carried out using

the Pix4Dmapping software. The above-mentioned

features were then deleted. Moreover, the water part

of the area was removed for elaboration purposes.

The cleaned UAV point cloud contained 14 227 points

(Figure 5).

Figure 5. A view of the cleaned UAV point cloud.

3 RESULTS

Based on the data recorded during the measurement

campaign in Zawory, a DTM was developed in the

Surfer software. The digital terrain model was created

using the IDP method [42]. The total number of points

used for the interpolation of the waterbody along

with the adjacent strip of land by the IDP method was

19 996. The IDP method was chosen because, for the

model of the waterbody adjacent to the beach along

with the strip of land in Gdynia, one of the highest

coefficients of determination (0.998) was obtained for

this method. Additionally, what is characteristic of the

IDP method is that the influence of measurement

points on the values of interpolated points, which are

more distant from the node, decreases with an

increase in the power exponent. This is of particular

importance when developing a topo-bathymetric

model based on a large dataset.

The IDP method is as follows [42]:

(

)

( )

∑

IDP

w xy z

z xy

w xy

(1)

where:

IDP(x,y) – height value of the interpolated (unknown)

point by the IDP method (m);

x, y – easting and northing of the interpolated

(unknown) point (m);

n –number of interpolating (known) points (–);

i – numbering representing successive interpolating

(known) points (–);

i(x,y) – weight value of the i-th point in the IDP

method (–);

i – height of the i-th measurement point (m).

The weight values are dependent on the

smoothing parameter [43]:

( )





w xy

d xy

(2)

where:

796

i(x,y) –distance between the interpolated (unknown)

point and the i-th point (m);

δ – smoothing parameter (m);

p – exponent (–).

In the model, the exponent p=1 was applied.

Moreover, a range of the data interpolating a

particular point was defined for the IDP (p=1)

method. It was established that the min. number of

interpolating points would be 16. The topo-

bathymetric chart generated using the Surfer software

by the IDP (p=1) method is presented on Figure 6.

Figure 6. A topo-bathymetric chart of the waterbody

adjacent to the beach along with the strip of land in Zawory,

developed based on bathymetric, LiDAR and

photogrammetric measurements using the IDP (p=1)

method (a) and its 3D visualisation (b).

In the generated GRID model, the min. depth

value was 153.909 m, while the max height value was

169.835 m. The accuracy of the interpolated DTM

(Figure 6) in relation to the measurements was

determined using the RMSE to be 0.089 m and the

Mean Absolute Error (MAE) to be 0.055 m. The

coefficient of determination was obtained at a level of

0.999, which means that the fit of the model to the

measurement data is very good. The difference

between the interpolated value and the measurement

value for the R68 measure is 0.055 m, while for the

R95 measure, it has a value of 0.187 m.

4 CONCLUSIONS

Multi-sensor integration of hydroacoustic and

optoelectronic data is a multi-stage task that should be

considered on a case-by-case basis, depending on the

input data. The article presents the methodology for

performing bathymetric, LiDAR and

photogrammetric measurements in the Lake Kłodno

coastal zone. Moreover, it describes all the stages of

bathymetric, LiDAR and photogrammetric data

elaboration. The conducted work resulted in the

development of a DTM.

A very important stage in the creation of DTM is

data elaboration. At the beginning, work was started

on the UAV point cloud. Initially, UAV data covered

the Lake Kłodno area along with the adjacent land.

However, in order to develop a digital terrain model,

only data on the surface are needed, which means that

the data recorded on buildings, trees and other built

features are redundant. Therefore, these data need to

be removed, which is possible through the

classification of objects. When creating the DTM, the

automatic classification of objects in the Pix4Dmapper

software was used. This was followed by work on the

bathymetric data. Here, the crucial stage was to clean

the data manually and refer them to the shoreline

height. In turn, the shoreline was drawn manually

based on the LiDAR data. As can be noticed, all the

described data elaboration stages included manual

data edition processes. This means that multi-sensor

data integration cannot be performed completely

automatically.

Based on the measurements performed, it can be

concluded that only complex surveys provide

sufficient data to create an accurate DTM. A digital

terrain model generated by the IDP method is

characterised by high accuracy. The difference

between the interpolated value and the measurement

value for the R68 measure is 0.055 m, while for the

R95 measure, it has a value of 0.187 m. Research has

shown that multi-sensor fusion of geospatial data

ensures the possibility of performing bathymetric,

LiDAR and photogrammetric measurements in the

coastal zone in accordance with the accuracy

requirements provided for the IHO Exclusive Order

(horizontal position error ≤ 1 m (p=0.95), vertical

position error ≤ 0.15 m (p=0.95)).

FUNDING

This research was funded by the National Centre for

Research and Development in Poland, grant number

LIDER/10/0030/L-11/19/NCBR/2020. Moreover, this research

was funded from the statutory activities of Gdynia Maritime

University, grant number WN/PI/2023/03.

REFERENCES

1. Abdalla, R. Introduction to Geospatial Information and

Communication Technology (GeoICT); Springer:

Berlin/Heidelberg, Germany, 2016; pp. 105–124.

2. Brivio, P.A.; Colombo, R.; Maggi, M.; Tomasoni, R.

Integration of Remote Sensing Data and GIS for

Accurate Mapping of Flooded Areas. Int. J. Remote Sens.

2002, 23, 429–441.

3. Brown, D.G.; Riolo, R.; Robinson, D.T.; North, M.; Rand,

W. Spatial Process and Data Models: Toward Integration

of Agent-based Models and GIS. J. Geogr. Syst. 2005, 7,

25–47.

4. Popielarczyk, D.; Templin, T. Application of Integrated

GNSS/Hydroacoustic Measurements and GIS

Geodatabase Models for Bottom Analysis of Lake

797

Hancza: The Deepest Inland Reservoir in Poland. Pure

Appl. Geophys. 2014, 171, 997–1011.

5. Cao, W.; Wong, M.H. Current Status of Coastal Zone

Issues and Management in China: A Review. Environ.

Int. 2007, 33, 985–992.

6. Cicin-Sain, B.; Knecht, R.W. Integrated Coastal and Ocean

Management: Concepts and Practices, 1st ed.; Island

Press: Washington, DC, USA, 1998.

7. Specht, M.; Specht, C.; Mindykowski, J.; Dąbrowski, P.;

Maśnicki, R.; Makar, A. Geospatial Modeling of the

Tombolo Phenomenon in Sopot Using Integrated

Geodetic and Hydrographic Measurement Methods.

Remote Sens. 2020, 12, 737.

8. Specht, M.; Stateczny, A.; Specht, C.; Widźgowski, S.;

Lewicka, O.; Wiśniewska, M. Concept of an Innovative

Autonomous Unmanned System for Bathymetric

Monitoring of Shallow Waterbodies (INNOBAT

System). Energies 2021, 14, 5370.

9. Lewicka, O.; Specht, M.; Stateczny, A.; Specht, C.; Brčić,

D.; Jugović, A.; Widźgowski, S.; Wiśniewska, M.

Analysis of GNSS, Hydroacoustic and Optoelectronic

Data Integration Methods Used in Hydrography.

Sensors 2021, 21, 7831.

10. Kang, M. Overview of the Applications of

Hydroacoustic Methods in South Korea and Fish

Abundance Estimation Methods. Fish. Aquat. Sci. 2014,

17, 369–376.

11. Makar, A. Determination of USV’s Direction Using

Satellite and Fluxgate Compasses and GNSS-RTK.

Sensors 2022, 22, 7895.

12. Makar, A. Method of Determination of Acoustic Wave

Reflection Points in Geodesic Bathymetric Surveys.

Annu. Navig. 2008, 14, 1–89.

13. Parente, C.; Vallario, A. Interpolation of Single Beam

Echo Sounder Data for 3D Bathymetric Model. Int. J.

Adv. Comput. Sci. Appl. 2019, 10, 6–13.

14. Specht, C.; Specht, M.; Dabrowski, P. Comparative

Analysis of Active Geodetic Networks in Poland. In

Proceedings of the 17th International Multidisciplinary

Scientific GeoConference (SGEM 2017), Albena,

Bulgaria, 27 June–6 July 2017.

15. Wlodarczyk-Sielicka, M.; Stateczny, A. Comparison of

Selected Reduction Methods of Bathymetric Data

Obtained by Multibeam Echosounder. In Proceedings of

the 2016 Baltic Geodetic Congress (BGC 2016), Gdańsk,

Poland, 2–4 June 2016.

16. Kondo, H.; Ura, T. Navigation of an AUV for

Investigation of Underwater Structures. Control Eng.

Pract. 2004, 12, 1551–1559.

17. Noureldin, A.; Karamat, T.B.; Georgy, J. Inertial

Navigation System. In Fundamentals of Inertial

Navigation, Satellite-based Positioning and Their

Integration; Springer: Berlin/Heidelberg, Germany, 2013;

pp. 125–166.

18. Specht, M. Method of Evaluating the Positioning System

Capability for Complying with the Minimum Accuracy

Requirements for the International Hydrographic

Organization Orders. Sensors 2019, 19, 3860.

19. Stateczny, A. Radar Water Level Sensors for Full

Implementation of the River Information Services of

Border and Lower Section of the Oder in Poland. In

Proceedings of the 17th International Radar Symposium

(IRS 2016), Kraków, Poland, 10–12 May 2016.

20. Wehr, A.; Lohr, U. Airborne Laser Scanning—An

Introduction and Overview. ISPRS J. Photogramm.

Remote Sens. 1999, 54, 68–82.

21. Williams, R.; Brasington, J.; Vericat, D.; Hicks, M.;

Labrosse, F.; Neal, M. Chapter Twenty–Monitoring

Braided River Change Using Terrestrial Laser Scanning

and Optical Bathymetric Mapping. In Developments in

Earth Surface Processes; Elsevier: Amsterdam,

Netherlands, 2011; Volume 15, pp. 507–532.

22. Liu, Y.; Wu, Z.; Zhao, D.; Zhou, J.; Shang, J.; Wang, M.;

Zhu, C.; Luo, X. Construction of High-resolution

Bathymetric Dataset for the Mariana Trench. IEEE

Access 2019, 7, 142441–142450.

23. Lubczonek, J.; Kazimierski, W.; Zaniewicz, G.; Lacka, M.

Methodology for Combining Data Acquired by

Unmanned Surface and Aerial Vehicles to Create Digital

Bathymetric Models in Shallow and Ultra-shallow

Waters. Remote Sens. 2022, 14, 105.

24. Masetti, G.; Andersen, O.; Andreasen, N.R.;

Christiansen, P.S.; Cole, M.A.; Harris, J.P.; Langdahl, K.;

Schwenger, L.M.; Sonne, I.B. Denmark’s Depth Model:

Compilation of Bathymetric Data within the Danish

Waters. Geomatics 2022, 2, 486-498.

25. Chief Inspectorate of Environmental Protection.

Assessment of the State of Lake Waterbodies in 2017-

2018 - table. Available online:

http://www.gios.gov.pl/pl/mkoopz/8-pms/99-jeziora

(accessed on 3 February 2023). (In Polish)

26. Kabiri, K. Accuracy Assessment of Near-shore

Bathymetry Information Retrieved from Landsat-8

Imagery. Earth Sci. Inform. 2017, 10, 235–245.

27. Menberu, Z.; Mogesse, B.; Reddythota, D. Evaluation of

Water Quality and Eutrophication Status of Hawassa

Lake Based on Different Water Quality Indices. Appl.

Water Sci. 2021, 11, 61.

28. Specht, C.; Specht, M.; Cywiński, P.; Skóra, M.; Marchel,

Ł.; Szychowski, P. A New Method for Determining the

Territorial Sea Baseline Using an Unmanned,

Hydrographic Surface Vessel. J. Coast. Res. 2019, 35,

925–936.

29. Specht, M.; Specht, C.; Lasota, H.; Cywiński, P.

Assessment of the Steering Precision of a Hydrographic

Unmanned Surface Vessel (USV) along Sounding

Profiles Using a Low-cost Multi-Global Navigation

Satellite System (GNSS) Receiver Supported Autopilot.

Sensors 2019, 19, 3939.

30. Specht, M.; Specht, C.; Szafran, M.; Makar, A.;

Dąbrowski, P.; Lasota, H.; Cywiński, P. The Use of USV

to Develop Navigational and Bathymetric Charts of

Yacht Ports on the Example of National Sailing Centre in

Gdańsk. Remote Sens. 2020, 12, 2585.

31. IHO. IHO Standards for Hydrographic Surveys, 6th ed.;

Special Publication No. 44; IHO: Monaco, Monaco, 2020.

32. Stateczny, A.; Błaszczak-Bąk, W.; Sobieraj-Żłobińska, A.;

Motyl, W.; Wisniewska, M. Methodology for Processing

of 3D Multibeam Sonar Big Data for Comparative

Navigation. Remote Sens. 2019, 11, 2245.

33. Stateczny, A.; Burdziakowski, P.; Najdecka, K.;

Domagalska-Stateczna, B. Accuracy of Trajectory

Tracking Based on Nonlinear Guidance Logic for

Hydrographic Unmanned Surface Vessels. Sensors 2020,

20, 832.

34. Stateczny, A.; Kazimierski, W.; Gronska-Sledz, D.;

Motyl, W. The Empirical Application of Automotive 3D

Radar Sensor for Target Detection for an Autonomous

Surface Vehicle’s Navigation. Remote Sens. 2019, 11,

1156.

35. Kacprzak, M.; Wodziński K. Execution of Photo Mission

by Manned Aircraft and Unmanned Aerial Vehicle.

Transactions of the Institute of Aviation 2016, 2, 130–141.

(In Polish)

36. Witek, M.; Jeziorska, J.; Niedzielski, T. Possibilities of

Using Unmanned Air Photogrammetry to Identify

Anthropogenic Transformations in River Channel.

Landform Analysis 2013, 24, 115–126. (In Polish)

37. Czaplewski, K.; Specht, C. Determination of Coast and

Base Line by GPS Techniques. Navigation and

Hydrography 2002, 14, 137–144.

38. Harley, M.D.; Turner, I.L.; Short, A.D.; Ranasinghe, R.

Assessment and Integration of Conventional, RTK-GPS

and Image-derived Beach Survey Methods for Daily to

Decadal Coastal Monitoring. Coast. Eng. 2011, 58, 194–

205.

39. Specht, C.; Weintrit, A.; Specht, M.; Dąbrowski, P.

Determination of the Territorial Sea Baseline—

798

Measurement Aspect. IOP Conf. Ser. Earth Environ. Sci.

2017, 95, 1–10.

40. Council of Ministers of the Republic of Poland.

Ordinance of the Council of Ministers of 15 October 2012

on the National Spatial Reference System; Council of

Ministers of the Republic of Poland: Warsaw, Poland,

2012. (In Polish)

41. Lewicka, O.; Specht, M.; Stateczny, A.; Specht, C.; Dyrcz,

C.; Dąbrowski, P.; Szostak, B.; Halicki, A.; Stateczny, M.;

Widźgowski, S. Analysis of Transformation Methods of

Hydroacoustic and Optoelectronic Data Based on the

Tombolo Measurement Campaign in Sopot. Remote

Sens. 2022, 14, 3525.

42. Ohlert, P.L.; Bach, M.; Breuer, L. Accuracy Assessment of

Inverse Distance Weighting Interpolation of

Groundwater Nitrate Concentrations in Bavaria

(Germany). Environ. Sci. Pollut. Res. 2022, 30, 9445–9455.

43. Tomczak, M., Spatial Interpolation and its Uncertainty

Using Automated Anisotropic Inverse Distance

Weighting (IDW) - Cross-Validation/Jackknife

Approach. Journal of Geographic Information and

Decision Analysis 1998, 2, 18–30.