Optical Properties Of Water Column Research Articles

The Optical Properties of Greater Florida Bay: Implications for Seagrass Abundance

Water column optical properties of Greater Florida Bay were investigated in the context of their impacts on seagrass distribution. Scattering played an important role in light attenuation throughout the shallow water system. The northwest region was characterized by an absence of seagrasses and the highest scattering by particles, mostly from resuspended carbonate sediments. Higher seagrass densities were observed in the open waters just north of the Florida Keys, where absorption coefficients were dominated by colored dissolved organic material and scattering was lower than in the northwest region. Patchy dense seagrass meadows were observed in the clear waters south of the Keys where scattering and absorption were low and contributed equally to light attenuation. In general, seagrasses were observed in areas where >7.5% of surface irradiance reached the plants and where optical properties were not dominated by scattering. Although the prevention of eutrophication and nuisance algal blooms may be necessary for preserving seagrass meadows in this system, our observations and model calculations indicate that nutrient control alone may be insufficient to permit seagrass recolonization if optical properties are dominated by particulate scattering from resuspended sediments.

Spectrally based remote sensing of river bathymetry

AbstractThis paper evaluates the potential for remote mapping of river bathymetry by (1) examining the theoretical basis of a simple, ratio‐based technique for retrieving depth information from passive optical image data; (2) performing radiative transfer simulations to quantify the effects of suspended sediment concentration, bottom reflectance, and water surface state; (3) assessing the accuracy of spectrally based depth retrieval under field conditions via ground‐based reflectance measurements; and (4) producing bathymetric maps for a pair of gravel‐bed rivers from hyperspectral image data. Consideration of the relative magnitudes of various radiance components allowed us to define the range of conditions under which spectrally based depth retrieval is appropriate: the remotely sensed signal must be dominated by bottom‐reflected radiance. We developed a simple algorithm, called optimal band ratio analysis (OBRA), for identifying pairs of wavelengths for which this critical assumption is valid and which yield strong, linear relationships between an image‐derived quantity X and flow depth d. OBRA of simulated spectra indicated that water column optical properties were accounted for by a shorter‐wavelength numerator band sensitive to scattering by suspended sediment while depth information was provided by a longer‐wavelength denominator band subject to strong absorption by pure water. Field spectra suggested that bottom reflectance was fairly homogeneous, isolating the effect of depth, and that radiance measured above the water surface was primarily reflected from the bottom, not the water column. OBRA of these data, 28% of which were collected during a period of high turbidity, yielded strong X versus d relations (R2 from 0·792 to 0·976), demonstrating that accurate depth retrieval is feasible under field conditions. Moreover, application of OBRA to hyperspectral image data resulted in spatially coherent, hydraulically reasonable bathymetric maps, though negative depth estimates occurred along channel margins where pixels were mixed. This study indicates that passive optical remote sensing could become a viable tool for measuring river bathymetry. Copyright © 2009 John Wiley & Sons, Ltd.

Seagrass Mapping in the Northern Gulf of Mexico using Airborne Hyperspectral Imagery: A Comparison of Classification Methods

Mapping areas of seagrass is important, in part because the extent of seagrass habitat can serve as a general indicator of coastal ecosystem health. While aerial photography and multispectral imagery have commonly been used to map seagrass, digital hyperspectral imagery is at the forefront of current mapping technology in many natural resource applications. In this study, HyMap hyperspectral imagery at 2.9-m resolution was used to map seagrass distribution off Horn Island, Mississippi, and estimate its areal coverage. Seagrass beds and sand-bottom classes were defined based on visual interpretation of the imagery coupled with field observations. Image spectra were sampled for each class in three water-depth zones determined by distance from shore. Supervised image classifications were performed using maximum likelihood, minimum distance to means, and spectral angle mapper methods to compare relative accuracies in mapping seagrass coverage. The maximum likelihood classification produced the highest overall accuracy of 83%. The spectral angle mapper yielded the lowest accuracy due to the predominant influence of water-column optical properties on the apparent spectral characteristics of seagrass and sand bottom.The ML classification indicated total seagrass coverage of 107 ha. This compared favorably with the results of a separate, independent study based on aerial photography acquired 1 day after the HyMap flyover. For tracking sea-grass coverage in the northern Gulf of Mexico, the mapping of individual seagrass patches at a spatial resolution of at least 3 m is recommended.

Influence of environmental conditions on late-summer cyanobacterial abundance in Saginaw Bay, Lake Huron

The relationships among environmental conditions and phytoplankton assemblages were characterized during late summer (2003-2005) in Saginaw Bay, Lake Huron. Differences among sampling stations, arising primarily from spatial disparities in water-column optical properties and nutrient availability, were evident. Cyanobacteria and diatoms dominated phytoplankton assemblages, with the greatest total chlorophyll a concentrations occurring at the innermost portions of the Bay. Microcystis abundance was greatest in the Bay's upper reaches and decreased with increasing distance from the mouth of the Saginaw River. A suite of variables, indicative of annually-distinct meteorological and hydrological conditions and phosphorus-laden inflows, were identified to (collectively) best ‘group’ stations in a manner consistent with that of phylogenetic-group chlorophyll a concentrations and cyanobacterial biovolumes. However, a great deal of variability between abiotic and biotic patterns remained unexplained and several abiotic variables singularly corresponded with Microcystis abundance. Taken together, it appears that multiple environmental conditions (including annual/episodic meteorological patterns, seasonal/intermittent riverine inflows, annual phosphorus loading, etc.) interact with taxon-specific physiological traits to holistically influence late-summer phytoplankton abundance throughout inner Saginaw Bay.

Enhancing the detection and classification of coral reef and associated benthic habitats: A hyperspectral remote sensing approach

Coral reefs and associated benthic habitats are heterogeneous in nature. A remote sensor designed to discriminate these environments requires a high number of narrow, properly placed bands which are not currently available in existing satellite sensors. Optical hyperspectral sensors mounted on aerial platforms seem to be appropriate for overcoming the lack of both high spectral and spatial resolution of satellite sensors. This research presents results of an innovative coral reef application by such a sensor. Using hyperspectral Airborne Imaging Spectroradiometer for Applications (AISA) Eagle data, the approach presented solves the confounding influence of water column attenuation on substrate reflectance on a per‐pixel basis. The hyperspectral imagery was used in band ratio algorithms to derive water depth and water column optical properties (e.g., absorption and backscattering coefficients). The water column correction technique produced a bottom albedo image which revealed that the dark regions comprised of sea grasses and benthic algae had albedo values ≈15%, whereas sand‐ and coral‐dominated areas had albedos >30% and ≈15–35%, respectively. The retrieved bottom albedo image was then used to classify the benthos, generating a detailed map of benthic habitats, followed by accuracy assessment.

Bathymetry, water optical properties, and benthic classification of coral reefs using hyperspectral remote sensing imagery

The complexity and heterogeneity of shallow coastal waters over small spatial scales provides a challenging environment for mapping and monitoring benthic habitats using remote sensing imagery. Additionally, changes in coral reef community structure are occurring on unprecedented temporal scales that require large-scale synoptic coverage and monitoring of coral reefs. A variety of sensors and analyses have been employed for monitoring coral reefs: this study applied a spectrum-matching and look-up-table methodology to the analysis of hyperspectral imagery of a shallow coral reef in the Bahamas. In unconstrained retrievals the retrieved bathymetry was on average within 5% of that measured acoustically, and 92% of pixels had retrieved depths within 25% of the acoustic depth. Retrieved absorption coefficients had less than 20% errors observed at blue wavelengths. The reef scale benthic classification derived by analysis of the imagery was consistent with the percent cover of specific coral reef habitat classes obtained by conventional line transects over the reef, and the inversions were robust as the results were similar when the benthic classification retrieval was constrained by measurements of bathymetry or water column optical properties. These results support the use of calibrated hyperspectral imagery for the rapid determination of bathymetry, water optical properties, and the classification of important habitat classes common to coral reefs.

A "Reef-Up" approach to classifying coral habitats from IKONOS imagery

Monitoring coral reef benthos with satellites has typically followed a sensor-down approach, with the classification algorithm driven by statistics derived from the imagery. I adopt a method, drawing on statistics derived from hyperspectral optical field measurements of substrate reflectance to train image classification. In order to calibrate the satellite data with direct physical measurements of reflectivity, it is necessary to process both the imagery and in situ data to common units of albedo. Building upon a proof-of-concept study conducted by the author in the Red Sea, the link is made by correcting the remote sensing data for the effect of varying bathymetry using in situ measurement of water column optical properties and a digital elevation model constructed from a vessel-based acoustic survey, thereby yielding units of substrate reflectance. Extensive ground verification of the predictive benthic habitat map resulting from image classification showed that eight substrate classes were resolved with an overall accuracy of 69% down to a depth of 6 m, including live and dead coral framework. As compared to conventional from-image classification techniques, the reef-up method offers the potential for higher thematic accuracy while maintaining a greater degree of flexibility for repeat survey using platforms of higher spectral and spatial resolution, expected to come online in the near future. The fact that image acquisition and optical ground-truthing did not occur concurrently, is of particular relevance in confirming that in situ measurements can be made independent of image acquisition and retrospectively linked to appropriate substrate classes. Considering the wealth of hyperspectral data already acquired for shallow reef facies, the work highlights the potential of the reef-up approach for quantifying substrate distribution in coral environments using both air- and spaceborne platforms.

Passive optical remote sensing of river channel morphology and in-stream habitat: Physical basis and feasibility

Successful monitoring of ecologically significant, vulnerable fluvial systems will require improved quantitative techniques for mapping channel morphology and in-stream habitat. In this study, we assess the ability of remote sensing to contribute to these objectives by (1) describing the underlying radiative transfer processes, drawing upon research conducted in shallow marine environments; (2) modeling the effects of water depth, substrate type, suspended sediment concentration, and surface turbulence; (3) quantifying the limitations imposed by finite detector sensitivity and linear quantization; and (4) evaluating two depth retrieval algorithms using simulated and field-measured spectra and archival imagery. The degree to which variations in depth and substrate can be resolved depends on bottom albedo and water column optical properties, and scattering by suspended sediment obscures substrate spectral features and reduces the resolution of depth estimates. Converting continuous radiance signals to discrete digital numbers implies that depth estimates take the form of contour intervals that become wider as depth increases and as bottom albedo and detector sensitivity decrease. Our results indicate that a simple band ratio can provide an image-derived variable that is strongly linearly related to water depth across a broad range of stream conditions. This technique outperformed the linear transform method used in previous stream studies, most notably for upwelling radiance spectra [ R 2=0.79 for the ln(560 nm/690 nm) ratio]. Applied to uncalibrated multispectral and hyperspectral images of a fourth-order stream in Yellowstone National Park, this flexible technique produced hydraulically reasonable maps of relative depth. Although radiometric precision and spatial resolution will impose fundamental limitations in practice, remote mapping of channel morphology and in-stream habitat is feasible and can become a powerful tool for scientists and managers.

Characterisation of Optical Water Quality in Bunaken National Marine Park, Indonesia

Effective passive optical remote sensing of submerged coral reef ecosystems requires not only appropriate atmospheric correction, but also water column correction. Algorithms accounting for atmospheric effects are fairly well established and readily available, but water column correction algorithms are still under development. Many approaches to water column correction assume horizontal homogeneity and strict adherence to Beer’s Law of logarithmic vertical attenuation, which may not be the case in many coral reef ecosystems. Water column optical properties were measured using a multispectral dropsonde radiometer in Bunaken National Marine Park, North Sulawesi, Indonesia, to examine the vertical and horizontal variability of light in a typical coral reef environment. This largely descriptive case study demonstrates the complexity of the interaction of light in shallow coastal environments with often highly reflective substrata and serves to warn against assumptions of water optical property homogeneity. Downwelling attenuation coefficients are provided for use in water column correction of future remote sensing missions.