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Remote Sens. 2016, 8(6), 487; doi:10.3390/rs8060487

Submerged Kelp Detection with Hyperspectral Data

1
Department of Geography, Kiel University, Ludewig-Meyn-Str. 14, Kiel 24118, Germany
2
Alfred-Wegener-Institute, Helmholtz Centre for Polar and Marine Research, Am Handelshafen 12, Bremerhaven 27570, Germany
*
Author to whom correspondence should be addressed.
Academic Editors: Deepak R. Mishra, Richard W. Gould and Prasad S. Thenkabail
Received: 31 March 2016 / Revised: 30 May 2016 / Accepted: 2 June 2016 / Published: 8 June 2016
(This article belongs to the Special Issue Remote Sensing in Coastal Environments)
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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. View Full-Text
Keywords: macroalgae; hyperspectral; coastal; airborne; kelp; imaging spectroscopy; AISA; Helgoland macroalgae; hyperspectral; coastal; airborne; kelp; imaging spectroscopy; AISA; Helgoland
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Uhl, F.; Bartsch, I.; Oppelt, N. Submerged Kelp Detection with Hyperspectral Data. Remote Sens. 2016, 8, 487.

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