Increased spectral resolution enhances coral detection under varying water conditions

Botha, Elizabeth C.; Brando, Vittorio E.; Anstee, Janet; Dekker, Arnold G.; Sagar, Stephen M.

doi:10.1016/j.rse.2012.12.021

Cited by 66 publications

(56 citation statements)

References 63 publications

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“…Clear to turbid water columns ( Figure 8, water types 1 and 4 respectively) were modeled to assess their impact on benthic class optical separation. For a given water clarity the number of optically distinguishable benthic classes decreased in a near exponential manner with increasing depth, as has been described in the literature [26,48]. Increasing the water turbidity also reduces the number of separable classes dramatically with increasing depth.…”

Section: Water-column Specific Benthic Spectral Librariessupporting

confidence: 67%

“…Employing such a spectral library into a physics-based inversion, for instance, may not give a realistic representation of benthic classification at increased depth and/or turbidity (discussed in Section 3.4) as fewer classes would be optically separable [26,48]. To include the attenuating properties of a water column and water depth we used the semi-analytical (SA) shallow water forward model given by Lee et al [16].…”

Section: Depth and Water Column Specific Spectral Librariesmentioning

confidence: 99%

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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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Section: Water-column Specific Benthic Spectral Librariessupporting

confidence: 67%

Section: Depth and Water Column Specific 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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“…Higher spectral resolution satellite data enhances substratum separability [19], leading to more accurate bathymetric and substratum retrieval [20]. In coastal waters [21] found that sensors with 20 nm contiguous bands in the 400-800 nm spectral region provide better results than sensors with a limited number of broader multispectral bands with regard to bathymetric depth retrieval.…”

Section: Factors Affecting Image Qualitymentioning

confidence: 99%

“…These bands are often highly correlated, thus offering limited information per band. Additional bands in the visible spectrum enhances the ability to retrieve bathymetry, benthic, and water quality characteristics [19,[22][23][24][25][26][27].…”

Section: Factors Affecting Image Qualitymentioning

confidence: 99%

Effects of Per-Pixel Variability on Uncertainties in Bathymetric Retrievals from High-Resolution Satellite Images

2016

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Increased sophistication of high spatial resolution multispectral satellite sensors provides enhanced bathymetric mapping capability. However, the enhancements are counter-acted by per-pixel variability in sunglint, atmospheric path length and directional effects. This case-study highlights retrieval errors from images acquired at non-optimal geometrical combinations. The effects of variations in the environmental noise on water surface reflectance and the accuracy of environmental variable retrievals were quantified. Two WorldView-2 satellite images were acquired, within one minute of each other, with Image 1 placed in a near-optimal sun-sensor geometric configuration and Image 2 placed close to the specular point of the Bidirectional Reflectance Distribution Function (BRDF). Image 2 had higher total environmental noise due to increased surface glint and higher atmospheric path-scattering. Generally, depths were under-estimated from Image 2, compared to Image 1. A partial improvement in retrieval error after glint correction of Image 2 resulted in an increase of the maximum depth to which accurate depth estimations were returned. This case-study indicates that critical analysis of individual images, accounting for the entire sun elevation and azimuth and satellite sensor pointing and geometry as well as anticipated wave height and direction, is required to ensure an image is fit for purpose for aquatic data analysis.

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“…While a range of these studies have recently used high spatial resolution data, e.g. IKONOS (Knudby et al 2013;Andrefouet et al 2003;Riegl and Purkis 2005;Elvidge et al 2004), Quickbird (Knudby et al 2013;Botha et al 2013;Mishra et al 2006), Worldview (Botha et al 2013), and CASI (Botha et al 2013;Mishra et al 2006), there are a *Corresponding author. E-mail: nurj_din@yahoo.com Article number of studies that have used medium spatial resolution data, e.g.…”

Section: Introductionmentioning

confidence: 99%

Multisensor and multitemporal data from Landsat images to detect damage to coral reefs, small islands in the Spermonde archipelago, Indonesia

et al. 2015

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Abstract− Coral reefs are important because of their high biodiversity and their key role in the tropical marine biosphere. Furthermore, coral reefs are very valuable as a socioeconomic resource as they make important contributions to the gross domestic product of many countries. Thus, it is very important to monitor dynamic spatial distributions of coral reefs and related habitats dominated by coral rubble, dead coral, and bleached corals. Despite these natural and socio-economic advantages, many factors are threatening coral reefs. The study site was selected in Spermonde archipelago, South Sulawesi, Indonesia because this area is included in the Coral Triangle, recognized as the epicenter of coral diversity and a priority for conservation. Images of Landsat MSS, Landsat TM, Landsat ETM, Landsat ETM+, and Landsat 8 data were used to examine changes in the coral reefs of Suranti Island in the Spermonde Archipelago during forty one years from 1972 to 2013. The image processing includes gap fills, atmospheric corrections, geometric corrections, image composites, water column corrections, unsupervised classifications, and reclassifications. Fill Gap processing was done on Landsat ETM+ SLC-off. Subsequently, a multi-component change detection procedure was applied to define changes. Shallow water bottom types classification was divided into live coral, rubble and sand habitats, dead coral with algae, rubble, and sand. Preliminary results showed significant changes during the period 1972-2013 as well as changes in coral reefs, likely explained partly by destructive fishing practices.

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Increased spectral resolution enhances coral detection under varying water conditions

Cited by 66 publications

References 63 publications

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

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

Effects of Per-Pixel Variability on Uncertainties in Bathymetric Retrievals from High-Resolution Satellite Images

Multisensor and multitemporal data from Landsat images to detect damage to coral reefs, small islands in the Spermonde archipelago, Indonesia

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