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Geosciences 2018, 8(2), 34; https://doi.org/10.3390/geosciences8020034

Mapping of Cold-Water Coral Carbonate Mounds Based on Geomorphometric Features: An Object-Based Approach

Geological Survey of Norway, Postal Box 6315 Torgarden, NO-7491 Trondheim, Norway
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Received: 14 December 2017 / Revised: 18 January 2018 / Accepted: 20 January 2018 / Published: 23 January 2018
(This article belongs to the Special Issue Marine Geomorphometry)
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Abstract

Cold-water coral reefs are rich, yet fragile ecosystems found in colder oceanic waters. Knowledge of their spatial distribution on continental shelves, slopes, seamounts and ridge systems is vital for marine spatial planning and conservation. Cold-water corals frequently form conspicuous carbonate mounds of varying sizes, which are identifiable from multibeam echosounder bathymetry and derived geomorphometric attributes. However, the often-large number of mounds makes manual interpretation and mapping a tedious process. We present a methodology that combines image segmentation and random forest spatial prediction with the aim to derive maps of carbonate mounds and an associated measure of confidence. We demonstrate our method based on multibeam echosounder data from Iverryggen on the mid-Norwegian shelf. We identified the image-object mean planar curvature as the most important predictor. The presence and absence of carbonate mounds is mapped with high accuracy. Spatially-explicit confidence in the predictions is derived from the predicted probability and whether the predictions are within or outside the modelled range of values and is generally high. We plan to apply the showcased method to other areas of the Norwegian continental shelf and slope where multibeam echosounder data have been collected with the aim to provide crucial information for marine spatial planning. View Full-Text
Keywords: cold-water coral; carbonate mound; habitat mapping; spatial prediction; image segmentation; geographic object-based image analysis; random forest; accuracy; confidence cold-water coral; carbonate mound; habitat mapping; spatial prediction; image segmentation; geographic object-based image analysis; random forest; accuracy; confidence
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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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Diesing, M.; Thorsnes, T. Mapping of Cold-Water Coral Carbonate Mounds Based on Geomorphometric Features: An Object-Based Approach. Geosciences 2018, 8, 34.

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