System wizualizacji łąk podwodnych i profili wgłębnych w kontekście geograficznym na podstawie akustycznych danych pomiarowych

 Zleceniodawca:Instytut Oceanologii PAN, Sopot, Pracownia Akustyki Morza Koordynator: dr Z. Łubniewski, mgr A. Partyka  Wstępna specyfikacja zadania projektowego: Opracowanie systemu do mapowania akustycznych danych pomiarowych (na temat roślinności podwodnej oraz dna morskiego) w kontekście geograficznym, z możliwością mozaikowania danych z sąsiednich obszarów i łączenia danych akustycznych z kartograficznymi oraz tworzenia gotowych map w formie widoków 2D i 3D

 

MAIN MOTIVATION:

to study the possibility of underwater meadow detection, biomass estimation species identification

in the Puck Bay (southern Baltic Sea) using hydroacoustic techniques

underwater meadows

HYDROACOUSTIC TECHNIQUES

echosounder

scattered sound

information on target

data collection

data processing

APPROACH

echosounder

DGPS

side scan sonar

camera

underwater meadows

side scan sonar

to detect and track the bottom

to detect and to characterize the vegetation

acoustical data:

1. Biosonics dual beam echosounder frequency: 208 kHz,

2. EdgeTech Side Scan Sonar DF-1000 frequency 100 and 500 kHz

precise position data :

DGPS TRIMBLE SE4000 frequency: 1 kHz,

horizontal accuracy: 0.3 – 1 m

biological data:

diver observation and sampling

underwater video recording

APPROACH

DATA ACQUISITION

PROCESSING THE POSITION – REFERENCED HYDROACOUSTICAL DATA

Site conditions

studied area - 500 m x 500 m size, northern part of the outer Puck Bay

bottom conditions - sand bottom

bathymetry - a very slight variability, mean depth about 2 m

patchy vegetation spatial distribution

dominant species – Zostera Marina, Zanichellia sp. and Potamogeton sp.

APPROACH

DIFFICULTIESCOMPLICATED BACKSCATTERING MECHANISM:

3.

“acoustically hard” sessile organisms

brown filamentous algae (Pilayella sp.) (eutrophication)

1.

2.

BUBBLES

UNDERWATER MEADOW DETECTION

THE DETECTION TECHNIQUE WAS DEVELOPED

SPECIES IDENTIFICATION

THE DATA WERE COLLECTED AND SOME PROCESSING

ALGORYTHMS WERE DEVELOPED

BIOMASS ESTIMATION

THE DATA WERE COLLECTED

RESULTS(down looking echosounder)

2. „Parametric” method

UNDERWATER MEADOWS DETECTION. METHODS

1. „Bottom tracking” method

3. Neural Net-based recognition method

RESULTS(down looking echosounder)

„BOTTOM TRACKING” METHOD

sandy bed

p

echosounder

pulsez1

vegetation

echosounder

z2

z1

t

sandy bottom

t2=2z2/c

t1=2z1/c

p

t

„BOTTOM TRACKING” METHOD

(i) detection (underwater meadows occurrence)

(ii) plant height measurement

(iii) mapping

possibilities

UNDERWATER MEADOWS DETECTION . METHODS

1. „Bottom tracking” method

3. Neural Net-based recognition method

2. „Parametric” method

“PARAMETRIC” METHOD,

first step - definition of important parameters

0.5

1

1.5

2

2.5-95 -90 -85 -80 -75 -70 -65 -60 -55

Depth [m]

Sv [dB]

0.5

1

1.5

2

2.5-95 -90 -85 -80 -75 -70 -65 -60 -55

Depth [m]

Sv [dB]

sandy bottom

vegetation

RESULTS(side scan sonar, mapping)

map of underwater vegetation spatial distribution May 2001

RESULTS(side scan sonar)

SEASONAL VARIABILITY

FUTURE WORK:

1. Species identification

2. Biomass estimation

3. Study of backscattering mechanism

pomiary 2001 - 2002

0

500

1000

1500

2000

2500

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14numer pomiaru

bio

ma

sa

[g

/m2

]

średnia biomasa całejpróby

średnia mokra masa traw y

średnia sucha masa traw y