Classification of benthic composition in a coral reef environment using spectral unmixing

Goodman, James; Ustin, Susan L.

doi:10.1117/1.2815907

Cited by 68 publications

(36 citation statements)

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“…The papers covered in the special section cover each element of the coastal aquatic zone, including: water surface and column properties, such as algal blooms [19,20 18], kelp [15], and the concentration of organic and inorganic material within the water column [13,14,18,21]; bathymetry [13,14,17,21] and benthic/substrate cover [16,17,21]. Two notable advances, evident from all of these papers, and distinct from past reviews on the status of remote sensing for mapping coastal aquatic environments, are: (1) the operational use of semianalytic algorithms to estimate depth/concentrations/reflectance; along with (2) the quantification and communication of accuracy and precision of the output maps of bathymetry, benthic/substrate composition and water quality parameters.…”

Section: Overview Of Papers In the Special Sectionmentioning

confidence: 99%

“…[13][14][15]17,21] demonstrate practical approaches for processing a variety of hyper-spectral images of optically shallow waters. The long time series of global ocean colour satellite data (i.e.…”

Section: Overview Of Papers In the Special Sectionmentioning

confidence: 99%

“…Clear demonstrations of management ready applications were presented for mapping and tracking algal blooms in the Baltic Sea [19], mapping the seasonal and spatial variations of water quality in one of the largest European fresh water bodies [20], monitoring changes in coral cover in areas subject to different management zonation and practices [16], and monitoring suspended sediment concentrations for active management of a shipping channel dredging operation [18]. Improved methods for baseline mapping and monitoring of benthic cover features were presented for coral reef and seagrass environments [13][14][15]17,21], for a range of environments in north-west Australia, Hawaii, Italy and Florida. These papers also demonstrated the value of hyper-spectral image data and semi-analytic algorithms for deriving bathymetry and water quality parameters, in addition to seafloor reflectance.…”

Section: Overview Of Papers In the Special Sectionmentioning

confidence: 99%

“…Goodman and Ustin [13] integrated a semi-analytical inversion model with a linear unmixing analysis to derive maps of depth and benthic composition from an airborne hyperspectral image for a coral reef environment in Kaneohe Bay, Hawaii.…”

Section: Overview Of Papers In the Special Sectionmentioning

confidence: 99%

“…This special section contains nine papers representing the state of the art of remote sensing applications in aquatic environments (inland, estuarine and coastal waters, seagrass and coral reefs) with a focus on papers clearly demonstrating monitoring and management applications [13][14][15][16][17][18][19][20][21].…”

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confidence: 99%

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Guest Editorial: Coastal Aquatic Remote Sensing Applications for Environmental Monitoring and Management

Brando

Phinn

2007

J. Appl. Remote Sens

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In all regions of the world, coastal aquatic environments are under increasing levels of environmental stress, due to the large number of people living immediately adjacent to them and using them as a resource. Information on the composition and condition of these water bodies and their benthic environments is a critical piece of knowledge infrastructure for development and application of coastal management practices [1][2][3]. Due to the large and dynamic nature of coastal aquatic environments, remote sensing offers a potential source for this information. However, if remote sensing is so useful and suitable data sets for mapping and monitoring coastal environments have been around since the Landsat era (1972) -why isn't it widely used in coastal resource management applications around the world?There are a number of potential answers to that question, related to suitability of technology, methods and the communication of true costs and capabilities of remote sensing based approaches. Bukata [4] offers the following reasons for the limited "operational" use of remote sensing for monitoring coastal environments:-Inadequate technology; -Inadequate science; -Inadequate or non-existent applications; -Inadequate communication of potential, results and products; -Lack of official standards for image products; -Cost of regular delivery is too high; -Satellite remote sensing is "suspect"; and -Society and/or organizational barriers exist. Very few papers, with the exception of large scale physical-biological oceanographic applications, published in scientific journals show a global uptake of the science and techniques of aquatic remote sensing into environmentally relevant monitoring and management applications. The majority of past reviews of remote sensing for coastal applications have focused on two areas: (1) improvements to technology, techniques and applications, with limited assessment of accuracy and true costs [1,3,[5][6][7][8][9][10][11] and (2) understanding light interactions in shallow water environments [6,12]. Both of these activities were essential to underpin the use of remote sensing for monitoring and managing coastal resources. We are now in a position where we can: collect suitable image data; apply robust models to transform the images to maps of biophysical parameters; collect field data to

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Section: Overview Of Papers In the Special Sectionmentioning

confidence: 99%

Section: Overview Of Papers In the Special Sectionmentioning

confidence: 99%

Section: Overview Of Papers In the Special Sectionmentioning

confidence: 99%

Section: Overview Of Papers In the Special Sectionmentioning

confidence: 99%

mentioning

confidence: 99%

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Guest Editorial: Coastal Aquatic Remote Sensing Applications for Environmental Monitoring and Management

Brando

Phinn

2007

J. Appl. Remote Sens

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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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References

2011

Remote Sensing and Global Environmental Change

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Classification of benthic composition in a coral reef environment using spectral unmixing

Cited by 68 publications

References 5 publications

Guest Editorial: Coastal Aquatic Remote Sensing Applications for Environmental Monitoring and Management

Guest Editorial: Coastal Aquatic Remote Sensing Applications for Environmental Monitoring and Management

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

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