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Spectral index development for mapping live coral cover

Joyce, Karen E. and Phinn, Stuart R. (2013). Spectral index development for mapping live coral cover. Journal of Applied Remote Sensing,7(1):073590-1-073590-20.

Document type: Journal Article
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IRMA ID 82057923xPUB442
Title Spectral index development for mapping live coral cover
Author Joyce, Karen E.
Phinn, Stuart R.
Journal Name Journal of Applied Remote Sensing
Publication Date 2013
Volume Number 7
Issue Number 1
ISSN 1931-3195   (check CDU catalogue open catalogue search in new window)
Scopus ID 2-s2.0-84904502143
Start Page 073590-1
End Page 073590-20
Total Pages 20
Place of Publication United States
Publisher SPIE- International Society for Optical Engineering
HERDC Category C1 - Journal Article (DIISR)
Abstract Remotely sensed spectral indices are used in a range of environments for estimating properties of vegetation, soil, atmospheric, and water features. Here, the development of an index sensitive to the amount of live coral, using in situ spectral reflectance data from Australia and Hawaii is outlined. From an initial spectral reflectance library of common reef benthic features, linear spectral mixing was used to create mixed reflectance signatures that represented image pixels in a reef environment. The correlation between the proportion of total live coral and the mixed reflectance signal at each wavelength was calculated to determine the wavelengths sensitive to variations in the amount of live coral. First and second derivatives of the reflectance spectra, in addition to simple band ratios, were also tested. The same processing and analysis procedures were then followed after simulating the spectral mixtures under different depths and levels of suspended organic content using a radiative transfer model (Hydrolight 4.1). Results show that the second derivative of reflectance at 564 nm was one of the wavelength regions most sensitive to variations in live coral cover and least sensitive to variations in water depth and quality. Subsequent research will present the applicability of this technique to hyperspectral image data.
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