1 / 19

MAIN MOTIVATION : to study the possibility of  underwater meadow detection,

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:

chloe-foley
Télécharger la présentation

MAIN MOTIVATION : to study the possibility of  underwater meadow detection,

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. 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

  2. underwater meadows MAIN MOTIVATION: to study the possibility of  underwater meadow detection,  biomass estimation  species identification in the Puck Bay (southern Baltic Sea)using hydroacoustic techniques

  3. echosounder information on target scattered sound HYDROACOUSTIC TECHNIQUES data collection data processing

  4. DGPS side scan sonar camera side scan sonar underwater meadows APPROACH echosounder

  5. APPROACH DATA ACQUISITION 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 PROCESSING THE POSITION – REFERENCED HYDROACOUSTICAL DATA to detect and track the bottom to detect and to characterize the vegetation

  6. APPROACH 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.

  7. DIFFICULTIES COMPLICATED BACKSCATTERING MECHANISM: 1. brown filamentous algae (Pilayella sp.) (eutrophication) 2. “acoustically hard” sessile organisms 3. BUBBLES

  8. RESULTS (down looking echosounder) 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

  9. UNDERWATER MEADOWS DETECTION. METHODS 3. Neural Net-based recognition method 1. „Bottom tracking” method RESULTS (down looking echosounder) 2. „Parametric” method

  10. „BOTTOM TRACKING” METHOD echosounder z2 z1 vegetation p p t2=2z2/c t1=2z1/c t echosounder pulse z1 sandy bottom sandy bed t

  11. „BOTTOM TRACKING” METHOD (i) detection (underwater meadows occurrence) (ii) plant height measurement (iii) mapping possibilities

  12. UNDERWATER MEADOWS DETECTION . METHODS 3. Neural Net-based recognition method 1. „Bottom tracking” method 2. „Parametric” method

  13. “PARAMETRIC” METHOD, first step - definition of important parameters vegetation sandy bottom

  14. RESULTS (side scan sonar, mapping) map of underwater vegetation spatial distribution May 2001

  15. RESULTS (side scan sonar) SEASONAL VARIABILITY

  16. FUTURE WORK: 1. Species identification 2. Biomass estimation 3. Study of backscattering mechanism

More Related