Submerged Kelp Detection with Hyperspectral Data.

Uhl, F., Bartsch, I. and Oppelt, N. (2016) Submerged Kelp Detection with Hyperspectral Data. Remote Sensing, 8 (6). DOI 10.3390/rs8060487.

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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.

Document Type: Article
Additional Information: Times Cited: 0 Uhl, Florian Bartsch, Inka Oppelt, Natascha
Keywords: macroalgae; hyperspectral; coastal; airborne; kelp; imaging spectroscopy; AISA; Helgoland
Research affiliation: Kiel University > Kiel Marine Science
OceanRep > The Future Ocean - Cluster of Excellence
Kiel University
Refereed: Yes
Open Access Journal?: No
Publisher: MDPI
Projects: Future Ocean
Date Deposited: 18 Mar 2017 08:39
Last Modified: 20 Aug 2019 11:56
URI: https://oceanrep.geomar.de/id/eprint/36354

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