Seagrass meadows and kelp forests are two of the most important marine habitats along the Norwegian coast. These are exposed to stressors such as eutrophication, ocean warming and ocean darkening, which all impact their distribution and health. At present, mapping of these species in Norway is done at a small number of sampling points using underwater “drop cameras”, recording coverage or state parameters of the species at points or along line transects. There is a need for cost efficient tools to map and monitor the distribution and ecological state of blue forests over larger areas and extended time periods. Large-scale mapping based on imaging from satellites or airplanes is possible, but has several drawbacks: Satellites have limited spatial resolution and depend on cloudless days, and airplane missions are costly. We propose using unmanned aerial vehicles (UAVs) equipped with hyperspectral cameras for mapping medium-sized areas. UAVs enable flexible, low-cost imaging missions with high spatial resolution, and hyperspectral imaging will provides detailed spectral information within each pixel. The spectrum of light reflected from underwater vegetation and the seafloor can be used as a “spectral fingerprint” to estimate parameters such as plant coverage, species, biomass and physiological state. Scuba divers, drop cameras and ROVs will be used to aquire “ground truth” measurement of these parameters, and machine learning methods will be used to train mathematical models relating the hyperspectral data to the field measurements. This enables estimation of biophysical parameters for each image pixel. Through statistical analysis of the mapped spatial and temporal changes, we will identify the main drivers that cause the observed patterns. Understanding how the structure and function of these species varies across environmental gradients is essential knowledge for sustainable coastal management.
Project leader: Martin Skjelvareid
Institution: Institutt for datateknologi og beregningsorienterte ingeniørfag