New coastal ocean remote sensing techniques permit benthic habitats to be explored with higher resolution than ever before. A mechanistic radiative transfer approach is developed that first removes the distorting influence of the water column on the remotely sensed signal to retrieve an estimate of the reflectance at the seafloor. The retrieved bottom reflectance is then used to classify the benthos. This spectrally based approach is advantageous because model components are separate and can be evaluated and modified individually for different environments. Here, we applied our approach to quantitatively estimate shallow-water bathymetry and leaf area index (LAI) of the seagrass Thalassia testudinum for a study site near Lee Stocking Island, Bahamas. Two high-resolution images were obtained from the ocean portable hyperspectral imager for low-light spectroscopy (Ocean PHILLS) over the study site in May 1999 and 2000. A combination of in situ observations of seafloor reflectance and radiative transfer modeling was used to develop and test our algorithm. Bathymetry was mapped to meter-scale resolution using a site-specific relationship (r 2 ϭ 0.97) derived from spectral ratios of remote sensing reflectance at 555 and 670 nm. Depth-independent bottom reflectance was retrieved from remote sensing reflectance using bathymetry and tables of modeled water column attenuation coefficients. The magnitude of retrieved bottom reflectance was highly correlated to seagrass LAI measured from diver surveys at seven stations within the image (r 2 ϭ 0.88-0.98). Mapped turtlegrass LAI was remarkably stable over a 2-yr period at our study site, even though Hurricane Floyd swept over the study site in September 1999.Managing and preserving coastal marine resources is a formidable challenge given the rapid pace of change affecting coastal environments. Fast, accurate, and quantitative tools are needed for detecting change in coastal ecosystems. Traditional in situ surveys are time and labor intensive, generally lack the spatial resolution and precision required to detect subtle changes before they become catastrophic, and can be difficult to maintain from year to year (Orth and Moore 1983;Peterson and Fourqurean 2001). Aerial photography provides more effective spatial coverage and has been used to semiquantitatively map benthic substrates (Ferguson et al. 1993;Kirkman 1996;Sheppard et al. 1995), but it is not effective at distinguishing color differences due to variations in water depth. The spectral reflectance obtained from digital remote sensing imagery represents a considerable advancement over conventional photography and allows AcknowledgmentsWe acknowledge the helpful comments from our anonymous reviewers and all of the many individuals who aided in collection of the in situ oceanographic field data (including S. Wittlinger, S. Palacios, M. Cummings). We also acknowledge J. Bowles, M. Kappus, and M. Carney for collecting the PHILLS data. Acknowledgements are extended to E. Boss and R. Zaneveld for supplying the IOPs, ...

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.

Abstract:The Ocean Portable Hyperspectral Imager for Low-Light Spectroscopy (Ocean PHILLS) is a hyperspectral imager specifically designed for imaging the coastal ocean. It uses a thinned, backsideilluminated CCD for high sensitivity and an all-reflective spectrograph with a convex grating in an Offner configuration to produce a nearly distortionfree image. The sensor, which was constructed entirely from commercially available components, has been successfully deployed during several oceanographic experiments in 1999-2001. Here we describe the instrument design and present the results of laboratory characterization and calibration. We also present examples of remote-sensing reflectance data obtained from the LEO-15 site in New Jersey that agrees well with ground-truth measurements. 138-170 (1996). 17. A. Offner, "Annular field systems and the future of optical microlithography," Opt. Eng. 26, 294-299 (1987). 18. P. Mouroulis, R. O. Green, and T. G. Chrien, "Design of pushbroom imaging spectrometers for optimum recovery of spectroscopic and spatial information," Appl. ©2002 Optical Society of America

Remote sensing is a valuable tool for rapid identification of benthic features in coastal environments. Past applications have been limited, however, by multispectral models that are typically difficult to apply when bottom types are heterogeneous and complex. We attempt to overcome these limitations by using a spectral library of remote sensing reflectance (R rs ), generated through radiative transfer computations, to classify image pixels according to bottom type and water depth. R rs spectra were calculated for water depths ranging from 0.5 to 20 m at 0.5-to 1.0-m depth intervals using measured reflectance spectra from sediment, seagrass, and pavement bottom types and inherent optical properties of the water. To verify the library, computed upwelling radiance and downwelling irradiance spectra were compared to field measurements obtained with a hyperspectral tethered spectral radiometer buoy (TSRB). Comparisons between simulated spectra and TSRB data showed close matches in signal shape and magnitude. The library classification method was tested on hyperspectral data collected using a portable hyperspectral imager for low light spectroscopy (PHILLS) airborne sensor near Lee Stocking Island, Bahamas. Two hyperspectral images were classified using a minimum-distance method. Comparisons with ground truth data indicate that library classification can be successful at identifying bottom type and water depth information from hyperspectral imagery. With the addition of diverse sediments types and different species of corals, seagrass, and algae, spectral libraries will have the potential to serve as valuable tools for identifying characteristic wavelengths that can be incorporated into bottom classification and bathymetry algorithms.Remote sensing has long been used to analyze terrestrial features, such as soil mineral content, foliage density and type, and surface elevation (Curran et al. 1992; PalaciosOrueta and Ustin 1998;Rollin and Milton 1998;Kokaly and Clark 1999). Satellite and airborne sensors are well suited to terrestrial observations in the visible and infrared range. These sensors are more limited, however, when used over oceans or lakes because of the low reflectance values of deep water (giving relatively poor signal-to-noise ratios) and the complexity of combined water and bottom signals in shallow water (Jerlov 1976). Most applications of marine remote sensing to date have been estimations of phytoplankton biomass and sea surface temperatures (SSTs). In these applications, it is generally assumed that all light from the ocean is either spectrally reflected from the upper several meters of the water column or thermally emitted from the first few millimeters at the surface. For biomass and SST applications in shallow water, visible radiation reflected from the bottom Acknowledgments

No abstract