Portable Hyperspectral Imager For Low Light Spectroscopy Research Articles

Noise Estimation of Remote Sensing Reflectance Using a Segmentation Approach Suitable for Optically Shallow Waters

This paper outlines a methodology for the estimation of the environmental noise equivalent reflectance in aquatic remote sensing imagery using an object-based segmentation approach. Noise characteristics of remote sensing imagery directly influence the accuracy of estimated environmental variables and provide a framework for a range of sensitivity, sensor specification, and algorithm design studies. The proposed method enables estimation of the noise equivalent reflectance covariance of remote sensing imagery through homogeneity characterization using image segmentation. The method is first tested on a synthetic data set with known noise characteristics and is successful in estimating the noise equivalent reflectance under a range of segmentation structures. Testing on a Portable Hyperspectral Imager for Low-Light Spectroscopy (PHILLS) hyperspectral image in a coral reef environment shows the method to produce comparable noise equivalent reflectance estimates in an optically shallow water environment to those previously derived in optically deep water. This method is of benefit in aquatic studies where homogenous regions of optically deep water were previously required for image noise estimation. The ability of the method to characterize the covariance of an image is of significant benefit when developing probabilistic inversion techniques for remote sensing.

High spatial resolution spectrometry of rafting macroalgae (Sargassum)

Data with 0.4-m spatial resolution acquired ~2 km off the southeast Florida coast using the airborne Portable Hyperspectral Imager for Low-Light Spectroscopy (PHILLS) have been analyzed with the objective of identifying drifting surface macroalgae (Sargassum) through its spectral signature in at-sensor radiance. The observed spectral features of Sargassum include a peak at a wavelength of ~0.570 µm and a photosynthetic 'red edge' between 0.673 and 0.699 µm. Sargassum also exhibits high radiance in the reflected near-infrared but is impacted by the atmospheric absorption bands of water vapor at 0.720 µm and oxygen at 0.756 µm. The spectral signature is clearest and largest in amplitude where the Sargassum occurs as small surface aggregations, or rafts, which tend to lie at the downwind ends of narrow Sargassum windrows. The quantity of floating Sargassum was estimated within a single pixel by linearly mixing a spectrum of Sargassum-free water with varying percentages of a spectrum from a pixel assumed completely filled with floating plants. For our study site about 2.3% of the ocean area is classified as having some Sargassum coverage, with pixels completely filled with Sargassum being rare (only 0.2% of the classified Sargassum pixels) and pixels with the least-resolvable amount of Sargassum (~10% filled) being the most common.

Bathymetric Retrieval From Hyperspectral Imagery Using Manifold Coordinate Representations

In this paper, we examine the accuracy of manifold coordinate representations as a reduced representation of a hyperspectral imagery (HSI) lookup table (LUT) for bathymetry retrieval. We also explore on a more limited basis the potential for using these coordinates for modeling other in water properties. Manifold coordinates are chosen because they are a data-driven intrinsic set of coordinates, which naturally parameterize nonlinearities that are present in HSI of water scenes. The approach is based on the extraction of a reduced dimensionality representation in manifold coordinates of a sufficiently large representative set of HSI. The manifold coordinates are derived from a scalable version of the isometric mapping algorithm. In the present and in our earlier works, these coordinates were used to establish an interpolating LUT for bathymetric retrieval by associating the representative data with ground truth data, in this case from a Light Detection and Ranging (LIDAR) estimate in the representative area. While not the focus of the present paper, the compression of LUTs could also be applied, in principle, to LUTs generated by forward radiative transfer models, and some preliminary work in this regard confirms the potential utility for this application. In this paper, we analyze the approach using data acquired by the Portable Hyperspectral Imager for Low-Light Spectroscopy (PHILLS) hyperspectral camera over the Indian River Lagoon, Florida, in 2004. Within a few months of the PHILLS overflights, Scanning Hydrographic Operational Airborne LIDAR Survey LIDAR data were obtained for a portion of this study area, principally covering the beach zone and, in some instances, portions of contiguous river channels. Results demonstrate that significant compression of the LUTs is possible with little loss in retrieval accuracy.

Airborne hyperspectral imaging of cyanobacteria accumulations in the Potomac River

High-resolution spectroscopy using the Portable Hyperspectral Imager for Low-Light Spectroscopy (PHILLS) was applied to the problem of detecting potentially harmful algae blooms in the coastal environment. Data were collected on two aircraft passes, 30-min apart, over the tidally influenced part of the Potomac River. Use of two wavelengths, 0.676 and 0.700 µm, permitted the detection of surface algae accumulations while avoiding the need for atmospheric corrections, which are problematic in Case-2 water. The analysis identified algal accumulations derived from frontal processes, and narrow, linearly coherent streaks, derived from Langmuir circulation. The streaks increased markedly in number between the two passes and formed a two-dimensional pattern across the river, consistent with the advection time of surface material into windrows. The effect of wind on the patches is primarily a local reorganization of the algal material into new streaks. Spectra from within the streaks compared to those from ambient water showed absorption characteristics consistent with the presence of cyanobacteria. This interpretation is reinforced by available in-situ data. This study illustrates the value of high spectral and temporal resolutions in observing the spatial distribution of the algae, in identifying dominant functional groups, and in understanding the response of the algae to physical forcing.

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.