Dr. Peter R. Fearns Profile

Dr. Peter R. Fearns

at Curtin Univ

SPIE Involvement:

Author

SPIE Journal Paper | 1 December 2007

Retrieving key benthic cover types and bathymetry from hyperspectral imagery

Wojciech Klonowski, Peter Fearns, Mervyn Lynch

JARS, Vol. 1, Issue 01, 011505, (December 2007) https://doi.org/10.1117/12.10.1117/1.2816113

KEYWORDS: Reflectivity, Water, Data modeling, Video, Remote sensing, Ocean optics, Absorption, Backscatter, Seaborgium, Hyperspectral imaging

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Proceedings Article | 5 November 2003 Paper

Modeling ocean color

Peter Fearns, Mervyn Lynch

Proceedings Volume 5155, (2003) https://doi.org/10.1117/12.512065

KEYWORDS: Reflectivity, Ocean optics, Absorption, Water, Monte Carlo methods, Sensors, Optical properties, Sun, Data modeling, Remote sensing

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Proceedings Article | 5 November 2003 Paper

Hyperspectral remote sensing of Western Australian coastal waters

Wojciech Klonowski, Mervyn Lynch, Peter Fearns, L. Clementson

Proceedings Volume 5155, (2003) https://doi.org/10.1117/12.512190

KEYWORDS: Reflectivity, Water, Remote sensing, Ocean optics, Sensors, Coastal modeling, Absorption, Backscatter, Radiometry, In situ metrology

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Hyperspectral remote sensing provides a particularly useful means of determining inherent optical properties of coastal waters where constituents other than phytoplankton add to the optical complexity of the water column. The substantial number of channels, about 200 for most hyperspectral sensors, enables many of the constituents within the water column to be identified spectrally. Additionally, in shallow water the water leaving radiance may include a signal reflected from the sea bottom. A hyperspectral radiometer was deployed on monthly oceanographic cruises off the coast near Perth, Western Australia, to make observations. Field measured reflectance spectra were used as input into a slightly modified version of a reflectance model developed by Lee et al, 1999. Products, including the concentrations of chlorophyll-a (Chl-a), coloured dissolved organic matter (CDOM), and suspended sediments (SS), as well as the water column depth (H), were extracted from the reflectance model by incorporating an optimisation technique. A Levenberg-Marquardt retrieval scheme was utilised in the optimisation. This scheme involved minimizing the difference between the modelled and measured spectral reflectance curves. Water samples were also collected on the monthly oceanographic cruises and used to determine the concentrations of chlorophyll-a, CDOM and SS. Water depth was measured using the boat's echo sounder. The model-derived products were compared to in situ measurements. The mean difference between model retrieved depth and in situ depth was 12.5 % or 1.4 m (R² = 0.98, N=11). Excluding two field measurements taken in the Marmion Marine Park, the mean RMS difference in depth was 7.6 % or 0.9 m (R² = 0.99, N=9). The mean RMS difference between retrieved Chl-a concentration and in situ measured Chl-a was 11.1 % or 0.044 mgm^-3(R² = 0.91, N=8). These preliminary results suggest that the reflectance model works well for depth and Chl-a retrieval for Western Australian coastal waters and their sandy substrate.

Proceedings Article | 6 February 1997 Paper

Retrieval of chlorophyll concentration via inversion of ocean reflectance: a modeling approach

Peter Fearns, Mervyn Lynch

Proceedings Volume 2963, (1997) https://doi.org/10.1117/12.266474

KEYWORDS: Reflectivity, Water, Ocean optics, Absorption, Monte Carlo methods, 3D modeling, Optical properties, Scattering, Algorithm development, Atmospheric modeling

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Conference Committee Involvement (1)

Remote Sensing of Inland, Coastal, and Oceanic Waters

18 November 2008 | Noumea, New Caledonia

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