Not logged in
PANGAEA.
Data Publisher for Earth & Environmental Science

Mielck, Finn; Bartsch, Inka; Hass, H Christian; Wölfl, Anne-Cathrin; Bürk, Dietmar; Betzler, Christian (2014): Single beam survey data and links to sea-bottom video files off Helgoland. PANGAEA, https://doi.org/10.1594/PANGAEA.831732, Supplement to: Mielck, F et al. (2014): Predicting spatial kelp abundance in shallow coastal waters using the acoustic ground discrimination system RoxAnn. Estuarine, Coastal and Shelf Science, 143, 1-11, https://doi.org/10.1016/j.ecss.2014.03.016

Always quote above citation when using data! You can download the citation in several formats below.

RIS CitationBibTeX CitationShow MapGoogle Earth

Abstract:
Kelp forests represent a major habitat type in coastal waters worldwide and their structure and distribution is predicted to change due to global warming. Despite their ecological and economical importance, there is still a lack of reliable spatial information on their abundance and distribution. In recent years, various hydroacoustic mapping techniques for sublittoral environments evolved. However, in turbid coastal waters, such as off the island of Helgoland (Germany, North Sea), the kelp vegetation is present in shallow water depths normally excluded from hydroacoustic surveys. In this study, single beam survey data consisting of the two seafloor parameters roughness and hardness were obtained with RoxAnn from water depth between 2 and 18 m. Our primary aim was to reliably detect the kelp forest habitat with different densities and distinguish it from other vegetated zones. Five habitat classes were identified using underwater-video and were applied for classification of acoustic signatures. Subsequently, spatial prediction maps were produced via two classification approaches: Linear discriminant analysis (LDA) and manual classification routine (MC). LDA was able to distinguish dense kelp forest from other habitats (i.e. mixed seaweed vegetation, sand, and barren bedrock), but no variances in kelp density. In contrast, MC also provided information on medium dense kelp distribution which is characterized by intermediate roughness and hardness values evoked by reduced kelp abundances. The prediction maps reach accordance levels of 62% (LDA) and 68% (MC). The presence of vegetation (kelp and mixed seaweed vegetation) was determined with higher prediction abilities of 75% (LDA) and 76% (MC). Since the different habitat classes reveal acoustic signatures that strongly overlap, the manual classification method was more appropriate for separating different kelp forest densities and low-lying vegetation. It became evident that the occurrence of kelp in this area is not simply linked to water depth. Moreover, this study shows that the two seafloor parameters collected with RoxAnn are suitable indicators for the discrimination of different densely vegetated seafloor habitats in shallow environments.
Project(s):
Coverage:
Median Latitude: 54.196505 * Median Longitude: 7.899585 * South-bound Latitude: 54.170905 * West-bound Longitude: 7.872080 * North-bound Latitude: 54.227175 * East-bound Longitude: 7.930280
Date/Time Start: 2011-02-17T13:32:08 * Date/Time End: 2011-06-17T16:11:50
Size:
2 datasets

Download Data

Download ZIP file containing all datasets as tab-delimited text (use the following character encoding: )