Speaker
Description
Brown-algal reefs are important marine habitats and potential contributors to Blue Carbon storage, yet their abundance and spatial distribution are difficult to assess over large areas. Optical observations provide detailed information on algal occurrence and morphology, but their spatial coverage is limited. To overcome this scaling challenge, we combine optical data from autonomous underwater vehicles (AUVs) and towed camera systems with hydroacoustic data.
Within a six-year monitoring project, two research cruises per year are conducted at three sites within German Baltic Sea Marine Protected Areas. The surveys generate thousands of images per cruise, complemented by video, hydroacoustic and biological sampling data. Together, these datasets form a large, heterogeneous and temporally repeated dataset capturing seasonal and interannual variability.
As the project is in its initial phase, at the current project stage the focus is on data acquisition, exploration and the development of an efficient data workflow. A key challenge is the management and analysis of the diverse and high-volume datasets. Automated image-detection methods are being investigated to support the future analysis of optical data. In parallel, we are exploring how optical observations can be related to hydroacoustic information to enable future extrapolation from localized, high-resolution observations to larger spatial scales. This work will provide the basis for a habitat-modelling framework as the project progresses.
We present the monitoring concept, data-acquisition strategy and data-integration workflow, together with example datasets and preliminary observations. The poster highlights the challenges and opportunities of integrating heterogeneous marine sensor data and exploring approaches for their automated analysis and future use in scalable habitat monitoring.