9 research outputs found
Recommended from our members
Interpretation of hyperspectral remote-sensing imagery by spectrum matching and look-up tables
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[subscript]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 Rrs 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.This is the publisher's version of record. The original submission is copyrighted by Optical Society of America and can be found here: http://www.opticsinfobase.org/ao/home.cf
Recommended from our members
Bidirectional reflectance measurements of sediments in the vicinity of Lee Stocking Island, Bahamas
In situ measurements of bidirectional reflectance factors (REFFs) are presented for submerged carbonate sediments at six sites in the vicinity of Lee Stocking Island. Sediment grain sizes ranged from 400 µm to ≫1000 µm. Several features were common to all data sets. Although overall sediment reflectance varied spectrally, normalized REFF was independent of wavelength within the natural sample variability. This allowed us to derive a model REFF which, when multiplied by REFF(Θi = 0°, Θr = 45°, Ø) at a specific wavelength, represented the data well. In addition, normally illuminated samples were almost Lambertian, but samples with larger grain sizes had an REFF that decreased with increasing view angles. As the illumination angle increased, samples became increasingly non‐Lambertian. The dominant feature of the REFF in these non‐Lambertian surfaces is in the backscattering direction. In this direction the REFF was significantly larger than the nadir value. The largest backscattering REFFs correspond to large grain sizes and increases with increasing illumination angles. The empirical model, which represents the data within one standard deviation of sample variation, is presented for these sediments. This model is well behaved at angles out to 90° and thus can be used in radiative transfer models. This model provides a realistic bottom reflectance that can be used to improve light field predictions in shallow water
Recommended from our members
Effects of microalgal communities on reflectance spectra of carbonate sediments in subtidal optically shallow marine environments
This study was conducted in subtidal areas around Lee Stocking Island, Bahamas, to investigate how microalgal biomass and community structure affect hyperspectral reflectance of sediments. Hyperspectral reflectance was measured on the surfaces of sediment cores collected from several types of carbonate sediments and habitats. Subsequently, photosynthetic and photoprotective pigments within the microalgae colonizing the top 5 mm of the sediment cores were quantified by high‐performance liquid chromatography (HPLC). Results of pigment analyses indicate that both microalgal biomass and community structure varied within and among sampling sites. Examination of spectral reflectance revealed differences both in the magnitude of overall reflectance between 400 and 710 nm and in the magnitude of absorption features. Second derivative analysis of reflectance spectra was used to identify nine narrow wavebands that correspond to wavelengths most affected by in vivo absorption by specific pigments. Results of linear regression analyses of the ratio of second derivatives at 676 nm to reflectance at 676 nm versus chlorophyll a plus chlorophyllide a indicate that total (living plus senescent or dead) microalgal biomass can be estimated from measurements of hyperspectral reflectance. Estimates of microalgal biomass can also be made based on the ratios of second derivatives at 444 nm to reflectance at 444 nm. Concentrations of other pigment groups can be estimated from second derivatives at 492 and 540 nm. These relationships between hyperspectral reflectance of sediments and benthic microalgal pigments suggest that remote sensing reflectance might be useful for distinguishing major differences among benthic habitats in some optically shallow areas
Recommended from our members
Optical remote sensing of benthic habitats and bathymetry in coastal environments at Lee Stocking Island, Bahamas: A comparative spectral classification approach
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 (Rrs), generated through radiative transfer computations, to classify image pixels according to bottom type and water depth. Rrs 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
Recommended from our members
Derivative analysis of absorption features in hyperspectral remote sensing data of carbonate sediments
This study uses derivative spectroscopy to assess qualitative and quantitative information regarding seafloor types that can be extracted from hyperspectral remote sensing reflectance signals. Carbonate sediments with variable concentrations of microbial pigments were used as a model system. Reflectance signals measured directly over sediment bottoms were compared with remotely sensed data from the same sites collected using an airborne sensor. Absorption features associated with accessory pigments in the sediments were lost to the water column. However major sediment pigments, chlorophyll a and fucoxanthin, were identified in the remote sensing spectra and showed quantitative correlation with sediment pigment concentrations. Derivative spectra were also used to create a simple bathymetric algorithm
Sediment properties influencing upwelling spectral reflectance signatures: The biofilm gel effect
Microbial communities often produce copious films of extracellular polymeric secretions (EPS) that may interact with sediments to influence spectral reflectance signatures of shallow marine sediments. We examined EPS associated with microbial mats to determine their potential effects on sediment reflectance properties. Distinct changes in spectral reflectance signatures of carbonate sediments from the Bahamas were observed among several sediment sites, which were specifically chosen for their presence of microbial mats and adjacent nonmat sediments. The presence of mats greatly reduced sediment reflectance signatures by ~10%‐20%, compared with adjacent nonmat areas having similar sediment characteristics. Decreases in reflectance near 444 and 678 nm could be attributed primarily to absorbance by photopigments. However, additional nonspecific decreases in reflectance occurred across a wide spectral range (400–750 nm). Experimental manipulations determined that nonspecific reflectance decreases were due to EPS that are produced by biofilm‐associated microorganisms of the mats. Microbial EPS, isolated from natural mat sediments exhibited small but nonspecific absorbances across a broad spectral range. When EPS was in relatively high concentrations, as in microbial mats, there was a “biofilm gel effect” on sediment reflectance properties. The effect was twofold. First, it increased the relative spacing of sediment grains, a process that permitted light to penetrate deeper into sediments. Second, it resulted in a more efficient capture of photons because of the change in refractive index of EPS gel itself relative to seawater. The relatively translucent EPS of biofilms, therefore, influenced the magnitude of reflectance across a broad spectral range in marine sediments
Recommended from our members
Interpretation of hyperspectral remote-sensing imagery by spectrum matching and look-up tables
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 (Rrs) 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 Rrs 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