Nearshore benthic habitat mapping based on multi-frequency, multibeam echosounder data using a combined object-based approach: a case study from the rowy site in the Southern Baltic Sea
Łukasz Janowski , Karolina Trzcińska , Jarosław Tęgowski , Aleksandra Kruss , Maria Rucińska-Zjadacz , Paweł Poćwiardowski
AbstractRecently, the rapid development of the seabed mapping industry has allowed researchers to collect hydroacoustic data in shallow, nearshore environments. Progress in marine habitat mapping has also helped to distinguish the seafloor areas of varied acoustic properties. As a result of these new developments, we have collected a multi-frequency, multibeam echosounder dataset from the valuable nearshore environment of the southern Baltic Sea using two frequencies: 150 kHz and 400 kHz. Despite its small size, the Rowy area is characterized by diverse habitat conditions and the presence of red algae, unique on the Polish coast of the Baltic Sea. This study focused on the utilization of multibeam bathymetry and multi-frequency backscatter data to create reliable maps of the seafloor. Our approach consisted of the extraction of 70 secondary features of bathymetric and backscatter data, including statistic and textural attributes of different scales. Based on groundtruth samples, we have identified six habitat classes and selected the most relevant features of the bathymetric and backscatter data. Additionally, five types of image processing pixel-based and object-based classifiers were tested. We also evaluated the performance of algorithms using an accuracy assessment based on the validation subset of the ground-truth samples. Our best results reached 93% overall accuracy and a kappa coefficient of 0.90, confirming that nearshore seabed habitats can be accurately distinguished based on multi-frequency, multibeam echosounder measurements. Our predictive habitat mapping of shallow euphotic zones creates a new scientific perspective and provides relevant data for the management of natural resources. Object-based approaches previously used in various environments and areas suggest that methodology presented in this study may be scalable.
|Journal series||Remote Sensing, ISSN 2072-4292, (A 35 pkt)|
|Publication size in sheets||1.05|
|Keywords in English||habitat mapping, multibeam echosounder, multi-frequency, image processing, feature selection, object-based image analysis|
|License||Journal (articles only); published final; ; with publication|
|Score|| = 35.0, 10-12-2018, ArticleFromJournal|
= 35.0, 10-12-2018, ArticleFromJournal
|Publication indicators||: 2016 = 3.244 (2) - 2016=3.749 (5)|
* presented citation count is obtained through Internet information analysis and it is close to the number calculated by the Publish or Perish system.