Identification of Kelp Habitats by Hyperspectral Remote Sensing Images in Turbid Coastal Waters Around Helgoland
Florian, Uhl1; Geisler, Tina1; N., Oppelt1; I., Bartsch2
1University of Kiel, GERMANY; 2AWI, GERMANY
Kelp habitats are assumed to be one of the most productive ecosystems on earth. Environmental changes, such as water temperature shifts, nutrient availability or changing wave strength, influence kelp growth and distribution. Kelp development studies require long-term, extensive and comparable data series. Information on the spatial distribution of Kelp can be provided using remote sensing techniques. These remote sensing techniques operate within the visual range of the electromagnetic spectrum, due to the absorption characteristics of the water. Difficulties occur at turbid water states which causing an increase in scattering of radiation. Some studies successfully mapping kelp habitats , but only under specific boundary conditions. In this context, we present a classification method to detect kelp forest in turbid coastal waters.
Hyperspectral airborne Aisaeagle data (spatial resolution: 2.8 m) is used to identify kelp forests in the sublitoral zone around the island Helgoland (Germany), North Sea. In this study, a slope based classification algorithm is applied on water column corrected data. Despite the low dynamic range of reflection intensities (kelp avg. 4 %, bottom sediment avg. 10 %, deep water avg. 5%), a distinction between kelp forest, optically deep water and bottom substrate was achieved. The slope based classification approach has high potential for its application for water column corrected data of coastal areas. The demand for reliable kelp propagation maps in turbid waters is strong in order to receive valuable new biological information.