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Remote Sens. 2015, 7(10), 13157-13189; doi:10.3390/rs71013157

A Method to Analyze the Potential of Optical Remote Sensing for Benthic Habitat Mapping

1
Remote Sensing and Satellite Research Group, Department of Physics and Astronomy, Curtin University, GPO Box U1987, Perth, WA 6845, Australia
2
School for the Environment, University of Massachusetts Boston, Boston, MA 02125, USA
3
Environmental Computer Science Ltd., Raymond Penny House, Hammett Square, Tiverton, Devon EX16 6LR, UK
4
Department of Environment and Agriculture, Curtin University, GPO Box U1987, Perth, WA 6845, Australia
*
Author to whom correspondence should be addressed.
Academic Editors: Deepak R. Mishra and Prasad S. Thenkabail
Received: 19 June 2015 / Revised: 23 September 2015 / Accepted: 29 September 2015 / Published: 2 October 2015
View Full-Text   |   Download PDF [6839 KB, uploaded 2 October 2015]   |  

Abstract

Quantifying the number and type of benthic classes that are able to be spectrally identified in shallow water remote sensing is important in understanding its potential for habitat mapping. Factors that impact the effectiveness of shallow water habitat mapping include water column turbidity, depth, sensor and environmental noise, spectral resolution of the sensor and spectral variability of the benthic classes. In this paper, we present a simple hierarchical clustering method coupled with a shallow water forward model to generate water-column specific spectral libraries. This technique requires no prior decision on the number of classes to output: the resultant classes are optically separable above the spectral noise introduced by the sensor, image based radiometric corrections, the benthos’ natural spectral variability and the attenuating properties of a variable water column at depth. The modeling reveals the effect reducing the spectral resolution has on the number and type of classes that are optically distinct. We illustrate the potential of this clustering algorithm in an analysis of the conditions, including clustering accuracy, sensor spectral resolution and water column optical properties and depth that enabled the spectral distinction of the seagrass Amphibolis antartica from benthic algae. View Full-Text
Keywords: shallow water benthic classification; hierarchical clustering; linear discriminant analysis; uncertainty propagation; hyperspectral; multispectral shallow water benthic classification; hierarchical clustering; linear discriminant analysis; uncertainty propagation; hyperspectral; multispectral
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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

Garcia, R.A.; Hedley, J.D.; Tin, H.C.; Fearns, P.R.C.S. A Method to Analyze the Potential of Optical Remote Sensing for Benthic Habitat Mapping. Remote Sens. 2015, 7, 13157-13189.

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