Shallow water substrate mapping using hyperspectral remote sensing

Fearns, Peter; Klonowski, Wojciech; Babcock, Russell C.; England, Philippa; Phillips, James D.

doi:10.1016/j.csr.2011.04.005

Cited by 53 publications

(28 citation statements)

References 23 publications

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“…17,25 The brown algae in the Baltic Sea showed broad reflectance peaks at 570, 600, and 650 nm corresponding to previous measurements in the world seas. 6,7,9,12,42 Red algae were discernible from other algal groups by the presence of a dip in reflectance at 570 nm. 10 In addition to chlorophyll a and carotenoids, the characteristic pigment in red algae is phycoerythrin.…”

Section: Discussionmentioning

confidence: 99%

In-air spectral signatures of the Baltic Sea macrophytes and their statistical separability

Kotta

Remm

Vahtmäe

et al. 2014

J. Appl. Remote Sens

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Abstract. Many macroalgal species potentially have distinctive spectral signatures detectable using remote sensing. In order to map the spatial distribution of these species, their spectral properties have to be quantified and statistical differences between species need to be assessed. In the present study, we collected a spectral library of the key benthic macrophyte species in the Baltic Sea area and presented the methodology that allows quantifying statistical differences between their reflectance spectra. The results indicate that three broad groups of algaegreen, brown, and red algae-can be separated based on their optical signatures. In general, the between-species differences are too small to allow easy recognition of benthic algae based on their untransformed reflectance spectra. However, the distinctness of the studied species and taxa improves if standardized reflectance values are used. The best indicative spectral range was at 530 to 570 nm for the separation of species and of larger taxonomic units. © The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

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Section: Discussionmentioning

confidence: 99%

In-air spectral signatures of the Baltic Sea macrophytes and their statistical separability

Kotta

Remm

Vahtmäe

et al. 2014

J. Appl. Remote Sens

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“…[2009]; BRUCE- Klonowski et al [2007]; SAMBUCA- Wettle and Brando [2006]) and CRISTAL have shown particular merit when applied to hyperspectral imagery (contiguous spectral bands, resolution $5 nm) Dekker et al, 2011;Fearns et al, 2011;Garcia et al, 2014a;Goodman and Ustin, 2007;Hedley et al, 2009;Klonowski et al, 2007;Lee et al, 1999;Lesser and Mobley, 2007]. However, these previous studies were designed primarily to demonstrate bathymetric retrieval and benthic classification capabilities for shallow waters, typically less than 10 m depth, with little emphasis on the derived IOP values and downstream geophysical products such as CHL, SPM, and water clarity measures.…”

Section: Key Pointsmentioning

confidence: 99%

A semianalytical ocean color inversion algorithm with explicit water column depth and substrate reflectance parameterization

et al. 2015

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A semianalytical ocean color inversion algorithm was developed for improving retrievals of inherent optical properties (IOPs) in optically shallow waters. In clear, geometrically shallow waters, light reflected off the seafloor can contribute to the water-leaving radiance signal. This can have a confounding effect on ocean color algorithms developed for optically deep waters, leading to an overestimation of IOPs. The algorithm described here, the Shallow Water Inversion Model (SWIM), uses pre-existing knowledge of bathymetry and benthic substrate brightness to account for optically shallow effects. SWIM was incorporated into the NASA Ocean Biology Processing Group's L2GEN code and tested in waters of the Great Barrier Reef, Australia, using the Moderate Resolution Imaging Spectroradiometer (MODIS) Aqua time series (2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012)(2013). SWIM-derived values of the total non-water absorption coefficient at 443 nm, a t (443), the particulate backscattering coefficient at 443 nm, b bp (443), and the diffuse attenuation coefficient at 488 nm, K d (488), were compared with values derived using the Generalized Inherent Optical Properties algorithm (GIOP) and the Quasi-Analytical Algorithm (QAA). The results indicated that in clear, optically shallow waters SWIM-derived values of a t (443), b bp (443), and K d (443) were realistically lower than values derived using GIOP and QAA, in agreement with radiative transfer modeling. This signified that the benthic reflectance correction was performing as expected. However, in more optically complex waters, SWIM had difficulty converging to a solution, a likely consequence of internal IOP parameterizations. Whilst a comprehensive study of the SWIM algorithm's behavior was conducted, further work is needed to validate the algorithm using in situ data.

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“…This has often led to the implementation of a spectral library of pure endmembers which would be linearly mixed either to pre-defined proportions such as in look-up table methods [14,15] or during spectral optimization in shallow-water inversion methods [16][17][18]. These methods have consequently achieved moderate to high benthic classification accuracies [14,[19][20][21].…”

Section: Introductionmentioning

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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Shallow water substrate mapping using hyperspectral remote sensing

Cited by 53 publications

References 23 publications

In-air spectral signatures of the Baltic Sea macrophytes and their statistical separability

In-air spectral signatures of the Baltic Sea macrophytes and their statistical separability

A semianalytical ocean color inversion algorithm with explicit water column depth and substrate reflectance parameterization

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

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