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    A method to analyze the potential of optical remote sensing for benthic habitat mapping

    238238_238238.pdf (3.863Mb)
    Access Status
    Open access
    Authors
    Garcia, R.
    Hedley, J.
    Tin, H.
    Fearns, Peter
    Date
    2015
    Type
    Journal Article
    
    Metadata
    Show full item record
    Citation
    Garcia, R. and Hedley, J. and Tin, H. and Fearns, P. 2015. A method to analyze the potential of optical remote sensing for benthic habitat mapping. Remote Sensing. 7: pp. 13157-13189.
    Source Title
    Remote Sensing
    DOI
    10.3390/rs71013157
    ISSN
    2072-4292
    School
    Department of Physics and Astronomy
    Remarks

    This open access article is distributed under the Creative Commons license http://creativecommons.org/licenses/by/4.0/

    URI
    http://hdl.handle.net/20.500.11937/15886
    Collection
    • Curtin Research Publications
    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.

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