Spectral Discrimination of Healthy and Non-Healthy Corals Based on Cluster Analysis, Principal Components Analysis, and Derivative Spectroscopy

Holden, H.; LeDrew, E.

doi:10.1016/s0034-4257(98)00029-7

Cited by 172 publications

(118 citation statements)

References 7 publications

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“…To remove noise due to environmental factors including capillary waves due to non-flat water surface and low signal in some wavelengths, spectra were smoothed 3 times with a Savitsky-Golay filter of order 3 and 15 nm width [5,12,25,26]. Standard deviation of R crl was also generated for each fragment from the imagery.…”

Section: Imaging and Reflectance Processingmentioning

confidence: 99%

“…The location of peaks in the 2nd derivative of reflectance has been linked to the absorption spectra of specific endosymbiont and host pigments and can be utilized to locate important reflectance features [12,[24][25][26]30,36]. The 2nd derivative of reflectance was calculated for each fragment at Day 0 and peaks located during processing.…”

Section: Reflectance Analysismentioning

confidence: 99%

“…Both studies attributed the relative resilience of the bay communities to thermal resistance in the animal and symbionts, and reduced irradiance due to high light attenuation in bay water compared to the clear oceanic waters found at fringe and patch reef sites. Bleaching in corals results in the flattening of reflectance features and an overall increase in the magnitude of R(λ) [24,25] as photosynthetic dinoflagellates are expelled and the bright, broadly reflective calcium carbonate skeleton influences reflectance.…”

Section: Spectral Response To Heatingmentioning

confidence: 99%

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Spectral Reflectance of Palauan Reef-Building Coral with Different Symbionts in Response to Elevated Temperature

et al. 2016

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Spectral reflectance patterns of corals are driven largely by the pigments of photosynthetic symbionts within the host cnidarian. The warm inshore bays and cooler offshore reefs of Palau share a variety of coral species with differing endosymbiotic dinoflagellates (genus: Symbiodinium), with the thermally tolerant Symbiodinium trenchii (S. trenchii) (= type D1a or D1-4) predominating under the elevated temperature regimes inshore, and primarily Clade C types in the cooler reefs offshore. Spectral reflectance of two species of stony coral, Cyphastrea serailia (C. serailia) and Pachyseris rugosa (P. rugosa), from both inshore and offshore locations shared multiple features both between sites and to similar global data from other studies. No clear reflectance features were evident which might serve as markers of thermally tolerant S. trenchii symbionts compared to the same species of coral with different symbionts. Reflectance from C. serailia colonies from inshore had a fluorescence peak at approximately 500 nm which was absent from offshore animals. Integrated reflectance across visible wavelengths had an inverse correlation to symbiont cell density and could be used as a relative indicator of the symbiont abundance for each type of coral. As hypothesized, coral colonies from offshore with Clade C symbionts showed a greater response to experimental heating, manifested as decreased symbiont density and increased reflectance or "bleaching" than their inshore counterparts with S. trenchii. Although no unique spectral features were found to distinguish species of symbiont, spectral differences related to the abundance of symbionts could prove useful in field and remote sensing studies.

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Section: Imaging and Reflectance Processingmentioning

confidence: 99%

Section: Reflectance Analysismentioning

confidence: 99%

Section: Spectral Response To Heatingmentioning

confidence: 99%

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Spectral Reflectance of Palauan Reef-Building Coral with Different Symbionts in Response to Elevated Temperature

et al. 2016

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“…The advantage of this approach is that no prior decision on the number of resultant clusters to output is needed. This is a common issue faced by conventional hierarchical clustering [39,40]. Here the number of output clusters is dependent on the following: (a) the magnitude of the error given by the covariance matrices-pertaining to NEΔrrs and the taxanomic spectral variability; (b) the spectral resolution of the sensor; and (c) the τm threshold.…”

Section: Hierarchical Clustering Using Linear Discriminant Coordinatementioning

confidence: 99%

“…We should note that unlike HICO, HyMap does not have bands below 450 nm [51], hence both sensors have wavebands that emphasize the spectral differences between the benthic classes analyzed. The literature has shown that specific wavelength ranges predominantly within 520-680 nm are useful for identifying substrate types, such as healthy coral, bleached coral, seagrass, sand and algae [34,39,50,52,53]. Therefore, it is likely that the difference in bandwidth is the cause where the spectral resolution of HICO and HyMap are 5.7 and 15 nm [51] respectively.…”

Section: Water-column Specific Benthic Spectral Librariesmentioning

confidence: 99%

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

Garcia

Hedley²,

Tín

et al. 2015

Remote Sensing

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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. OPEN ACCESSRemote Sens. 2015, 7 13158

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Hyperspectral Remote Sensing with Emphasis on Land Cover Mapping: From Ground to Satellite Observations

Petropoulos

Manevski

Carlson

2014

Scale Issues in Remote Sensing

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Spectral Discrimination of Healthy and Non-Healthy Corals Based on Cluster Analysis, Principal Components Analysis, and Derivative Spectroscopy

Cited by 172 publications

References 7 publications

Spectral Reflectance of Palauan Reef-Building Coral with Different Symbionts in Response to Elevated Temperature

Spectral Reflectance of Palauan Reef-Building Coral with Different Symbionts in Response to Elevated Temperature

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

Hyperspectral Remote Sensing with Emphasis on Land Cover Mapping: From Ground to Satellite Observations

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