Citation: Specht, M.; Wi´sniewska, M.;
Stateczny, A.; Specht, C.; Szostak, B.;
Lewicka, O.; Stateczny, M.;
Wid´zgowski, S.; Halicki, A. Analysis
of Methods for Determining Shallow
Waterbody Depths Based on Images
Taken by Unmanned Aerial Vehicles.
Sensors 2022, 22, 1844. https://
doi.org/10.3390/s22051844
Academic Editor: Pasquale Daponte
Received: 18 January 2022
Accepted: 22 February 2022
Published: 25 February 2022
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Perspective
Analysis of Methods for Determining Shallow Waterbody
Depths Based on Images Taken by Unmanned Aerial Vehicles
Mariusz Specht
1
, Marta Wi´sniewska
1
, Andrzej Stateczny
2,
* , Cezary Specht
3
, Bartosz Szostak
2
,
Oktawia Lewicka
3
, Marcin Stateczny
1
, Szymon Wid´zgowski
1
and Armin Halicki
1
1
Marine Technology Ltd., Wiktora Roszczynialskiego 4-6, 81-521 Gdynia, Poland;
m.specht@marinetechnology.pl (M.S.); m.wisniewska@marinetechnology.pl (M.W.);
m.stateczny@marinetechnology.pl (M.S.); s.widzgowski@marinetechnology.pl (S.W.);
a.halicki@marinetechnology.pl (A.H.)
2
Department of Geodesy, Gda´nsk University of Technology, Gabriela Narutowicza 11-12,
80-233 Gda´nsk, Poland; bartosz.szostak@pg.edu.pl
3
Department of Geodesy and Oceanography, Gdynia Maritime University, Morska 81-87,
81-225 Gdynia, Poland; c.specht@wn.umg.edu.pl (C.S.); o.lewicka@wn.umg.edu.pl (O.L.)
* Correspondence: andrzej.stateczny@pg.edu.pl
Abstract:
Hydrographic surveys enable the acquisition and processing of bathymetric data, which
after being plotted onto nautical charts, can help to ensure safety of navigation, monitor changes in
the coastal zone, and assess hydro-engineering structure conditions. This study involves the measure-
ment of waterbody depth, identification of the seabed shape and geomorphology, the coastline course,
and the location of underwater obstacles. Hydroacoustic systems mounted on vessels are commonly
used in bathymetric measurements. However, there is also an increasing use of Unmanned Aerial
Vehicles (UAV) that can employ sensors such as LiDAR (Light Detection And Ranging) or cameras
previously not applied in hydrography. Current systems based on photogrammetric and remote
sensing methods enable the determination of shallow waterbody depth with no human intervention
and, thus, significantly reduce the duration of measurements, especially when surveying large wa-
terbodies. The aim of this publication is to present and compare methods for determining shallow
waterbody depths based on an analysis of images taken by UAVs. The perspective demonstrates
that photogrammetric techniques based on the SfM (Structure-from-Motion) and MVS (Multi-View
Stereo) method allow high accuracies of depth measurements to be obtained. Errors due to the
phenomenon of water-wave refraction remain the main limitation of these techniques. It was also
proven that image processing based on the SfM-MVS method can be effectively combined with
other measurement methods that enable the experimental determination of the parameters of signal
propagation in water. The publication also points out that the Lyzenga, Satellite-Derived Bathymetry
(SDB), and Stumpf methods allow satisfactory depth measurement results to be obtained. However,
they require further testing, as do methods using the optical wave propagation properties.
Keywords:
bathymetric measurements; shallow waterbody; photogrammetric image; Unmanned
Aerial Vehicle (UAV); Unmanned Aerial System (UAS)
1. Introduction
Bathymetric works that enable the measurement of shallow waterbody depths is neces-
sary for acquiring geospatial information describing the marine environment. Information
on a waterbody’s depth has a direct effect on navigational safety and efficiency, coastal
zone management, the process of hydro-engineering structure designing and monitoring,
and a range of other types of human activity at sea [1,2].
The most popular devices for waterbody depth measurement include Single Beam
Echo Sounders (SBES) and MultiBeam EchoSounders (MBES). Even though SBES echo
sounders continue to be the most commonly used bathymetric systems worldwide, it is the
Sensors 2022, 22, 1844. https://doi.org/10.3390/s22051844 https://www.mdpi.com/journal/sensors
