Airborne hyperspectral detection of microbial mat pigmentation in Rangiroa atoll (French Polynesia)

Andréfouët, Serge; Payri, Claude; Hochberg, Eric J.; Mao, Lydie; Atkinson, Marlin J.

doi:10.4319/lo.2003.48.1_part_2.0426

Cited by 31 publications

(17 citation statements)

References 17 publications

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“…Hochberg & Atkinson 2000, Andréfouët et al 2003a,b, Combe et al 2005, Deronde et al 2006. HS imaging has an enormous potential for resolving biomass and distribution of photosynthetic microorganisms in complex microbial communities.…”

Section: Hyperspectral Imagingmentioning

confidence: 99%

Functional and structural imaging of phototrophic microbial communities and symbioses

Kühl¹,

Polerecký²

2008

Aquat. Microb. Ecol.

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In recent years, new imaging methods for mapping microhabitats, solute concentrations, microbial activity and photopigment content have been developed and increasingly applied in different areas of aquatic microbial ecology and biogeochemistry. Such techniques exhibit a spatial resolution similar to that of microsensors, but allow for instantaneous mapping of the 2-dimensional distribution and dynamics of solute and pigment concentrations over areas ranging from µm 2 to several cm 2 . Methods have been developed for imaging O 2 and CO 2 concentrations as well as pH with optical sensor foils (i.e. planar optodes), and for combined imaging of photopigments and photosynthetic activity via variable chlorophyll fluorescence. More recently, hyperspectral imaging has been employed to identify pigment content in individual cells and to visualize the spatial organization of functional groups in photosynthetic microbial communities. We give an overview of these emerging methods with a range of illustrative examples to demonstrate how they can provide useful information in functional and structural studies of a variety of aquatic microbial communities, such as sediments, biofilms, coral tissues or photosynthetic mats.

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Section: Hyperspectral Imagingmentioning

confidence: 99%

Functional and structural imaging of phototrophic microbial communities and symbioses

Kühl¹,

Polerecký²

2008

Aquat. Microb. Ecol.

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“…Hochberg and Atkinson 2 used Advanced Airborne Hyperspectral Imaging System imagery, and Andréfouët et al 3 used the Compact Airborne Spectrometer Imager for mapping and classification of benthic types into corals, algae, and sediments by fourth-derivative analysis of remotely sensed reflectance spectra. Louchard et al 4 used derivative analysis to classify carbonate sediments.…”

Section: Introductionmentioning

confidence: 99%

Interpretation of hyperspectral remote-sensing imagery by spectrum matching and look-up tables

Mobley¹,

Sundman²,

et al. 2005

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A spectrum-matching and look-up-table (LUT) methodology has been developed and evaluated to extract environmental information from remotely sensed hyperspectral imagery. The LUT methodology works as follows. First, a database of remote-sensing reflectance ͑R rs ͒ spectra corresponding to various water depths, bottom reflectance spectra, and water-column inherent optical properties (IOPs) is constructed using a special version of the HydroLight radiative transfer numerical model. Second, the measured R rs spectrum for a particular image pixel is compared with each spectrum in the database, and the closest match to the image spectrum is found using a least-squares minimization. The environmental conditions in nature are then assumed to be the same as the input conditions that generated the closest matching HydroLight-generated database spectrum. The LUT methodology has been evaluated by application to an Ocean Portable Hyperspectral Imaging Low-Light Spectrometer image acquired near Lee Stocking Island, Bahamas, on 17 May 2000. The LUT-retrieved bottom depths were on average within 5% and 0.5 m of independently obtained acoustic depths. The LUT-retrieved bottom classification was in qualitative agreement with diver and video spot classification of bottom types, and the LUT-retrieved IOPs were consistent with IOPs measured at nearby times and locations.

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“…In the field of benthic microbial ecology, which is the focus of this paper, large-scale spectral imaging techniques generally aim to identify pigments or to quantify biomass concentrations in microbial communities spread over several meters to kilometers, such as intertidal flats (5,6,17,20). Such techniques usually employ airborne imagers to detect reflected light in several tens of spectral bands covering visible and near-infrared regions.…”

mentioning

confidence: 99%

Modular Spectral Imaging System for Discrimination of Pigments in Cells and Microbial Communities

Polerecký

Bissett

Al-Najjar

et al. 2009

Appl Environ Microbiol

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Here we describe a spectral imaging system for minimally invasive identification, localization, and relative quantification of pigments in cells and microbial communities. The modularity of the system allows pigment detection on spatial scales ranging from the single-cell level to regions whose areas are several tens of square centimeters. For pigment identification in vivo absorption and/or autofluorescence spectra are used as the analytical signals. Along with the hardware, which is easy to transport and simple to assemble and allows rapid measurement, we describe newly developed software that allows highly sensitive and pigment-specific analyses of the hyperspectral data. We also propose and describe a number of applications of the system for microbial ecology, including identification of pigments in living cells and high-spatial-resolution imaging of pigments and the associated phototrophic groups in complex microbial communities, such as photosynthetic endolithic biofilms, microbial mats, and intertidal sediments. This system provides new possibilities for studying the role of spatial organization of microorganisms in the ecological functioning of complex benthic microbial communities or for noninvasively monitoring changes in the spatial organization and/or composition of a microbial community in response to changing environmental factors.Spectral imaging is a technique in which spectral information (i.e., the spectrum of light that is scattered from, transmitted through, or emitted by an object) is acquired at every location in an image. Since the spectral information reflects the object's identity, status, and/or composition, combining it with spatial information (i.e., the size, shape, and location of the object) enhances our ability to unravel and understand possible links between the spatial organization and functional relationships for constituents of a system. These attributes have made spectral imaging important in various areas of basic research and in industrial applications.Generally, previously described methods concentrated either on very large scales (e.g., astronomy and satellite or airborne remote sensing of the Earth) or on very small scales (e.g., microscopic observations in medicine and microbiology). In the field of benthic microbial ecology, which is the focus of this paper, large-scale spectral imaging techniques generally aim to identify pigments or to quantify biomass concentrations in microbial communities spread over several meters to kilometers, such as intertidal flats (5,6,17,20). Such techniques usually employ airborne imagers to detect reflected light in several tens of spectral bands covering visible and near-infrared regions. The spectral reflectance data are calibrated and validated by combining ground truth measurements of the parameters of interest (e.g., pigment content) with the spectral reflectance measurements obtained using single-point spectrometers, which detect the signals from regions whose areas are several square centimeters to several square decimeters. Quali...

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Airborne hyperspectral detection of microbial mat pigmentation in Rangiroa atoll (French Polynesia)

Cited by 31 publications

References 17 publications

Functional and structural imaging of phototrophic microbial communities and symbioses

Functional and structural imaging of phototrophic microbial communities and symbioses

Interpretation of hyperspectral remote-sensing imagery by spectrum matching and look-up tables

Modular Spectral Imaging System for Discrimination of Pigments in Cells and Microbial Communities

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