2015
DOI: 10.3390/rs71013157
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A Method to Analyze the Potential of Optical Remote Sensing for Benthic Habitat Mapping

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 forwa… Show more

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Cited by 44 publications
(33 citation statements)
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References 65 publications
(94 reference statements)
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“…The similarity of the reflectance spectra from distinct host species and symbionts further supports past remote sensing studies employing inversion of a representative endmember (where an optical model is applied to field reflectance data) to identify coral and benthic constituents [1][2][3][4]71,72]. The relationship between integrated reflectance and symbiont concentration may prove useful to assess or monitor reef condition and the potential for bleaching.…”
Section: Conclusion and Outlook For Remote Sensingsupporting
confidence: 49%
“…The similarity of the reflectance spectra from distinct host species and symbionts further supports past remote sensing studies employing inversion of a representative endmember (where an optical model is applied to field reflectance data) to identify coral and benthic constituents [1][2][3][4]71,72]. The relationship between integrated reflectance and symbiont concentration may prove useful to assess or monitor reef condition and the potential for bleaching.…”
Section: Conclusion and Outlook For Remote Sensingsupporting
confidence: 49%
“…Figure 4 shows the classification accuracy (i.e., error matrix diagonals-see Tables S1-S6 in Supplementary Materials) of each benthic class for the six depth-specific BSP-classifiers. The general trend of this figure is a decrease in classification accuracy as the depth increases, which is expected as the R rs between benthic classes become less distinct with increasing depth [27]. Up to 6 m, the classifiers generally have high classification accuracy (>90%) for Seagrass, Turf Algae, Calcareous Algae and all coral classes.…”
Section: Validation Datamentioning
confidence: 90%
“…Here, Calcareous Algae refers to crustose coralline algae. Note that (1) Green algae is not included in the classifier because it is unlikely to dominate an 8 × 8 m pixel and can be optically inseparable from seagrass at depth as noted by Garcia et al [27] (2) Soft coral has also excluded due its spectral similarity with Brown Coral [28] and (3) reflectance spectra for Rubble was not available and thus not included, though it is ecologically important in a coral reef ecosystem. Hochberg et al [28] describes the spectral features present in Figure 2.…”
Section: Workflowmentioning
confidence: 99%
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“…There are numerous studies that have used highresolution satellite images for identifying and mapping coastal habitats such as coral reefs (Benfield et al, 2007), sea grass meadows (Guimarães et al, 2011), mangroves (Heenkenda et al, 2014;Ibrahim et al, 2015), macroalgae (Garcia et al, 2015;Tin, O'Leary, and Fotedar, 2015), and freshwater/ salt marsh (Carle, Wang, and Sasser, 2014). However, these studies mostly utilized sensors with fewer than four spectral bands in the visible domain, which limited the detailed classification of vegetation (Feilhauer et al, 2013).…”
Section: Introductionmentioning
confidence: 99%