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Remote Sens. 2011, 3(5), 975-1005;

Remote Sensing of Shallow Coastal Benthic Substrates: In situ Spectra and Mapping of Eelgrass (Zostera marina) in the Gulf Islands National Park Reserve of Canada

Spectral Lab, Department of Geography, University of Victoria, P.O. Box 3060, STN CSC, Victoria, BC V8W 3R4, Canada
Parks Canada Office at Gulf Islands, National Park Reserve of Canada, 2220 Harbour Road, Sidney, BC V8L 2P6, Canada
Author to whom correspondence should be addressed.
Received: 28 February 2011 / Revised: 5 May 2011 / Accepted: 6 May 2011 / Published: 16 May 2011
(This article belongs to the Special Issue Remote Sensing in Coastal Ecosystem)
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Eelgrass (Zostera marina) is a keystone component of inter- and sub-tidal ecosystems. However, anthropogenic pressures have caused its populations to decline worldwide. Delineation and continuous monitoring of eelgrass distribution is an integral part of understanding these pressures and providing effective coastal ecosystem management. A proposed tool for such spatial monitoring is remote imagery, which can cost- and time-effectively cover large and inaccessible areas frequently. However, to effectively apply this technology, an understanding is required of the spectral behavior of eelgrass and its associated substrates. In this study, in situ hyperspectral measurements were used to define key spectral variables that provide the greatest spectral separation between Z. marina and associated submerged substrates. For eelgrass classification of an in situ above water reflectance dataset, the selected variables were: slope 500–530 nm, first derivatives (R’) at 566 nm, 580 nm, and 602 nm, yielding 98% overall accuracy. When the in situ reflectance dataset was water-corrected, the selected variables were: 566:600 and 566:710, yielding 97% overall accuracy. The depth constraint for eelgrass identification with the field spectrometer was 5.0 to 6.0 m on average, with a range of 3.0 to 15.0 m depending on the characteristics of the water column. A case study involving benthic classification of hyperspectral airborne imagery showed the major advantage of the variable selection was meeting the sample size requirements of the more statistically complex Maximum Likelihood classifier. Results of this classifier yielded eelgrass classification accuracy of over 85%. The depth limit of eelgrass spectral detection for the AISA sensor was 5.5 m. View Full-Text
Keywords: eelgrass; seagrass; remote sensing; hyperspectral; feature selection eelgrass; seagrass; remote sensing; hyperspectral; feature selection

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This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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O’Neill, J.D.; Costa, M.; Sharma, T. Remote Sensing of Shallow Coastal Benthic Substrates: In situ Spectra and Mapping of Eelgrass (Zostera marina) in the Gulf Islands National Park Reserve of Canada. Remote Sens. 2011, 3, 975-1005.

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