Gia Lamela

Optical scattering and backscattering by organic and inorganic particulates in U.S. coastal waters

We present the results of a study of optical scattering and backscattering of particulates for three coastal sites that represent a wide range of optical properties that are found in U.S. near-shore waters. The 6000 scattering and backscattering spectra collected for this study can be well approximated by a power-law function of wavelength. The power-law exponent for particulate scattering changes dramatically from site to site (and within each site) compared with particulate backscattering where all the spectra, except possibly the very clearest waters, cluster around a single wavelength power-law exponent of -0.94. The particulate backscattering-to-scattering ratio (the backscattering ratio) displays a wide range in wavelength dependence. This result is not consistent with scattering models that describe the bulk composition of water as a uniform mix of homogeneous spherical particles with a Junge-like power-law distribution over all particle sizes. Simultaneous particulate organic matter (POM) and particulate inorganic matter (PIM) measurements are available for some of our optical measurements, and site-averaged POM and PIM mass-specific cross sections for scattering and backscattering can be derived. Cross sections for organic and inorganic material differ at each site, and the relative contribution of organic and inorganic material to scattering and backscattering depends differently at each site on the relative amount of material that is present.

Manifold learning techniques for the analysis of hyperspectral ocean data

A useful technique in hyperspectral data analysis is dimensionality reduction, which replaces the original high dimensional data with low dimensional representations. Usually this is done with linear techniques such as linear mixing or principal components (PCA). While often useful, there is no a priori reason for believing that the data is actually linear. Lately there has been renewed interest in modeling high dimensional data using nonlinear techniques such as manifold learning (ML). In ML, the data is assumed to lie on a low dimensional, possibly curved surface (or manifold). The goal is to discover this manifold and therefore find the best low dimensional representation of the data. Recently, researchers at the Naval Research Lab have begun to model hyperspectral data using ML. We continue this work by applying ML techniques to hyperspectral ocean water data. We focus on water since there are underlying physical reasons for believing that the data lies on a certain type of nonlinear manifold. In particular, ocean data is influenced by three factors: the water parameters, the bottom type, and the depth. For fixed water and bottom types, the spectra that arise by varying the depth will lie on a nonlinear, one dimensional manifold (i.e. a curve). Generally, water scenes will contain a number of different water and bottom types, each combination of which leads to a distinct curve. In this way, the scene may be modeled as a union of one dimensional curves. In this paper, we investigate the use of manifold learning techniques to separate the various curves, thus partitioning the scene into homogeneous areas. We also discuss ways in which these techniques may be able to derive various scene characteristics such as bathymetry.

A new data-driven approach to modeling coastal bathymetry from hyperspectral imagery using manifold coordinates

Recently a new approach to modeling nonlinear structure in hyperspectral imagery was introduced [Bachmann et al., 2005]. The new method is a data-driven approach which extracts a set of coordinates that directly parameterize nonlinearities present in hyperspectral imagery, both on land and in the water column. The motivation for such a parameterization and its applicability to coastal bathymetry is based on the physical expectation that in shallow waters in a region that is homogeneous in bottom type and dissolved constituents, the reflectance at any particular wavelength should decay exponentially as a function of depth. If the rate varies with wavelength, then the reflectance should best be described by a nonlinear sheet or manifold in spectral space. Other changes in the structure of the data manifold can be expected as inherent optical properties (IOP) and bottom type vary. The manifold coordinates can be used to extract information concerning the latter as well. In the present work, we compare a manifold coordinate based approach to extracting bathymetry with prior work [Maness et al., 2005] based on radiative transfer modeling; the latter defined a set of look-up tables produced by repeated execution of a radiative transfer software package known as EcoLight. Comparative results for the two approaches are presented for the same Portable Hyperspectral Imager for low-light spectroscopy (PHILLS) airborne hyperspectral scene, acquired over the Indian River Lagoon in Florida in July 2004 and described in [Maness et al., 2005].

Modeling Coastal Waters from Hyperspectral Imagery using Manifold Coordinates

In [1] [2], we introduced a direct data driven method of modeling nonlinear structure in hyperspectral imagery based on Isometric Mapping [15]. More recently, we have further improved the scaling of the approach [2], making it a practical method for large-scale hyperspectral scenes. The new method extracts a set of data manifold coordinates that directly parameterize nonlinearities present in hyperspectral imagery, both on land and in the water column. In the water column, this is particularly important because of the nonlinear, attenuating properties of the medium. In this paper, we model hyperspectral imagery acquired by the NRL PHILLS [5] at the Indian River Lagoon, Florida in July 2004. In our previous efforts [3] using a small subset of data derived from the surf zone outside of the lagoon, dominant manifold coordinates were shown to parameterize bathymetry directly with a high degree of correlation to a radiative transfer look-up table (LUT) approach. In the present work, we construct a full scene manifold coordinate representation and use this as the basis of a LUT for samples with known depths as determined by the SHOALS LIDAR. Sequestered test data presented to the manifold based LUT yield a mean estimated depth which differs from the LOAR retrieved depth by less than 0.44 m for depths between 0-10 m with a standard deviation less than 1.2 m.

A profiling optics and water return system for validation and calibration of ocean color imagery

We describe a Profiling Optical and Water Return (POWR) system that has been developed and used extensively at sea. The POWR system is a collection of oceanographic instruments used to measure the inherent optical properties (IOPs) of the upper 100m of the ocean while simultaneously collecting up to eight water samples at various depths for chemical and biological analysis. IOPs are local measurements that are directly related to the properties of the water at the depth sampled; hence it is critical that the water samples be taken at the same time and location as the IOPs. Used during three major experiments, the POWR system has proven valuable for relating IOPs to in-water constituents in support of ocean color remote sensing data product validation, optical model validation, and other interdisciplinary programs.

Airborne system for multispectral, multiangle polarimetric imaging

In this paper, we describe the design, fabrication, calibration, and deployment of an airborne multispectral polarimetric imager. The motivation for the development of this instrument was to explore its ability to provide information about water constituents, such as particle size and type. The instrument is based on four 16 MP cameras and uses wire grid polarizers (aligned at 0°, 45°, 90°, and 135°) to provide the separation of the polarization states. A five-position filter wheel provides for four narrow-band spectral filters (435, 550, 625, and 750 nm) and one blocked position for dark-level measurements. When flown, the instrument is mounted on a programmable stage that provides control of the view angles. View angles that range to ±65° from the nadir have been used. Data processing provides a measure of the polarimetric signature as a function of both the view zenith and view azimuth angles. As a validation of our initial results, we compare our measurements, over water, with the output of a Monte Carlo code, both of which show neutral points off the principle plane. The locations of the calculated and measured neutral points are compared. The random error level in the measured degree of linear polarization (8% at 435) is shown to be better than 0.25%.

Hyperspectral imaging of an inter-coastal waterway

This paper demonstrates the characterization of the water properties, bathymetry, and bottom type of the Indian River Lagoon (IRL) on the eastern coast of Florida using hyperspectral imagery. Images of this region were collected from an aircraft in July 2004 using the Portable Hyperspectral Imager for Low Light Spectroscopy (PHILLS). PHILLS is a Visible Near InfraRed (VNIR) spectrometer that was operated at an altitude of 3000 m providing 4 m resolution with 128 bands from 400 to 1000 nm. The IRL is a well studied water body that receives fresh water drainage from the Florida Everglades and also tidal driven flushing of ocean water through several outlets in the barrier islands. Ground truth measurements of the bathymetry of IRL were acquired from recent sonar and LIDAR bathymetry maps as well as water quality studies concurrent to the hyperspectral data collections. From these measurements, bottom types are known to include sea grass, various algae, and a gray mud with water depths less than 6 m over most of the lagoon. Suspended sediments are significant (~35 mg/m3) with chlorophyll levels less than 10 mg/m3 while the absorption due to Colored Dissolved Organic Matter (CDOM) is less than 1 m-1 at 440 nm. Hyperspectral data were atmospherically corrected using an NRL software package called Tafkaa and then subjected to a Look-Up Table (LUT) approach which matches hyperspectral data to calculated spectra with known values for bathymetry, suspended sediments, chlorophyll, CDOM, and bottom type.

Impact of specular reflection on bottom type retrieved from WorldView-2 images

The Naval Research Laboratory (NRL) has been developing the Coastal Water Spectral Toolkit (CWST) to estimate water depth, bottom type and water column constituents such as chlorophyll, suspended sediments and chromophoric dissolved organic matter from hyperspectral imagery. The CWST uses a look-up table approach, comparing remote sensing reflectance spectra observed in an image to a database of modeled spectra for pre-determined water column constituents, depth and bottom type. Recently the CWST was modified to process multi-spectral WorldView-2 imagery. Generally imagery processed through the CWST has been collected under optimal sun and viewing conditions so as to minimize surface effects such as specular reflection. As such, in our standard atmospheric correction process we do not include a specular reflection correction. In June 2010 a series of 7 WorldView-2 images was collected within 2 minutes over Moreton Bay, Australia. The images clearly contain varying amounts of surface specular reflection. Each of the 7 images was processed through the CWST using identical processing to evaluate the impact of ignoring specular reflection on coverage and consistency of bottom types retrieved.

Influence of aerosol estimation on coastal water products retrieved from HICO images

The Hyperspectral Imager for the Coastal Ocean (HICO™) is a hyperspectral sensor which was launched to the International Space Station in September 2009. The Naval Research Laboratory (NRL) has been developing the Coastal Water Signatures Toolkit (CWST) to estimate water depth, bottom type and water column constituents such as chlorophyll, suspended sediments and chromophoric dissolved organic matter from hyperspectral imagery. The CWST uses a look-up table approach, comparing remote sensing reflectance spectra observed in an image to a database of modeled spectra for pre-determined water column constituents, depth and bottom type. In order to successfully use this approach, the remote sensing reflectances must be accurate which implies accurately correcting for the atmospheric contribution to the HICO top of the atmosphere radiances. One tool the NRL is using to atmospherically correct HICO™ imagery is Correction of Coastal Ocean Atmospheres (COCOA), which is based on Tafkaa 6S. One of the user input parameters to COCOA is aerosol optical depth or aerosol visibility, which can vary rapidly over short distances in coastal waters. Changes to the aerosol thickness results in changes to the magnitude of the remote sensing reflectances. As such, the CWST retrievals for water constituents, depth and bottom type can be expected to vary in like fashion. This work is an illustration of the variability in CWST retrievals due to inaccurate aerosol thickness estimation during atmospheric correction of HICO™ images.

Automated land-cover models of barrier islands in the Virginia Coast Reserve derived from multi-season HyMAP and PROBE2 imagery

This paper addresses the problem of automated land-cover classification in a chain of barrier islands, collectively known as the Virginia Coast Reserve (VCR), an NSF Long Term Ecological Research site. Previously we have reported on the development of automatic land-cover models derived from a HyMAP scene acquired in May 2000 of Smith Island, Virginia, one of the VCR islands. The present work considers five of the six islands covered by additional hyper-spectral collections from another similar sensor, PROBE2. PROBE2 was flown over the VCR on August 22 and October 18, 2001. Between the two sensors, we now have three-season coverage of Smith Island, and two-season coverage of five other VCR islands: Myrtle, Ship Shoals, Wreck, Cobb, and Hog. Automatic classification Models have been derived, using both exploratory unsupervised classification models, and supervised models derived from labelled spectra. Results have been compared with in situ spectrometry measurements taken by us at the VCR and at another site in Great Bay, NJ. We also have acquired PROBE2 imagery in August 2001 at the Great Bay site and collected in-situ spectra there in late July and early August. These data serve as the beginning point of cross-region comparisons.