Abstract
Submerged marine forests of macroalgae known as kelp are one of the key structures for coastal ecosystems worldwide. These communities are responding to climate driven habitat changes and are therefore appropriate indicators of ecosystem status and health. Hyperspectral remote sensing provides a tool for a spatial kelp habitat mapping. The difficulty in optical kelp mapping is the retrieval of a significant kelp signal through the water column. Detecting submerged kelp habitats is challenging, in particular in turbid coastal waters. We developed a fully automated simple feature detection processor to detect the presence of kelp in submerged habitats. We compared the performance of this new approach to a common maximum likelihood classification using hyperspectral AisaEAGLE data from the subtidal zones of Helgoland, Germany. The classification results of 13 flight stripes were validated with transect diving mappings. The feature detection showed a higher accuracy till a depth of 6 m (overall accuracy = 80.18%) than the accuracy of a maximum likelihood classification (overall accuracy = 57.66%). The feature detection processor turned out as a time-effective approach to assess and monitor submerged kelp at the limit of water visibility depth.
Highlights
Kelp ecosystems dominate approximately 25% of the world’s rocky shores [1]
Experimental studies have shown that the contribution of submerged aquatic vegetation (SAV) to the measured remote sensing signal decreases with increasing depth of the water column over the vegetation, resulting in a diminished spectral signal of the target species [37], whereas absolute reflectances of emergent macroalgae, like kelp and other brown algae, significantly increase with desiccation [38]
The spatial resolution of the AisaEAGLE data is higher than the transect mappings with an approximate radius of three metres around each mapping point (Figure 3); on average, 38 pixels cover the same area as a single diving mapping point
Summary
Kelp ecosystems dominate approximately 25% of the world’s rocky shores [1]. Kelps belong to the brown algae of the order Laminariales and form submerged forests of macroalgae. Experimental studies have shown that the contribution of submerged aquatic vegetation (SAV) to the measured remote sensing signal decreases with increasing depth of the water column over the vegetation, resulting in a diminished spectral signal of the target species [37], whereas absolute reflectances of emergent macroalgae, like kelp and other brown algae, significantly increase with desiccation [38]. The narrow bandwidths of forthcoming hyperspectral missions like EnMAP [46] or PRISMA [47] provide a better spectral resolution in the visible wavelength range This enables the detection of local features for kelp and macroalgae detection in general [48].
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