We present a two-step algorithm for the detection of seafloor propeller seagrass scars in shallow water using panchromatic images. The first step is to classify image pixels into scar and non-scar categories based on a sparse coding algorithm. The first step produces an initial scar map in which false positive scar pixels may be present. In the second step, local orientation of each detected scar pixel is computed using the morphological directional profile, which is defined as outputs of a directional filter with a varying orientation parameter. The profile is then utilized to eliminate false positives and generate the final scar detection map. We applied the algorithm to a panchromatic image captured at the Deckle Beach, Florida using the WorldView2 orbiting satellite. Our results show that the proposed method can achieve <90% accuracy on the detection of seagrass scars.
Current ocean color sensors, for example SeaWiFS and MODIS, are well suited for sampling the open ocean. However,
coastal environments are spatially and optically more complex and require more frequent sampling and higher spatial
resolution sensors with additional spectral channels. We have conducted experiments with data from Hyperion and
airborne hyperspectral imagers to evaluate these needs for a variety of coastal environments. Here we present results
from an analysis of airborne hyperspectral data for a Harmful Algal Bloom in Monterey Bay. Based on these results and
earlier studies we recommend increased frequency of sampling, increased spatial sampling and additional spectral
channels for ocean color sensors for coastal environments.
HyperSpectral Imagery (HSI) of the coastal zone often focuses on the estimation of bathymetry. However, the estimation of bathymetry requires knowledge, or the simultaneous solution, of water column Inherent Optical Properties (IOPs) and bottom reflectance. The numerical solution to the simultaneous set of equations for bathymetry, IOPs, and bottom reflectance places high demands on the spectral quality, calibration, atmospheric correction, and Signal-to-Noise (SNR) of the HSI data stream.
In October of 2002, a joint FERI/NRL/NAVO/USACE HSI/LIDAR experiment was conducted off of Looe Key, FL. This experiment yielded high quality HSI data at a 2 m resolution and bathymetric LIDAR data at a 4 m resolution. The joint data set allowed for the advancement and validation of a previously generated Look-Up-Table (LUT) approach to the simultaneous retrieval of bathymetry, IOPs, and bottom type. Bathymetric differences between the two techniques were normally distributed around a 0 mean, with the exception of two peaks. One peak related to a mechanical problem in the LIDAR detector mirrors that causes errors on the edges of the LIDAR flight lines. The other significant difference occurred in a single geographic area (Hawk Channel) suggesting an incomplete IOP or bottom reflectance description in the LUT data base. In addition, benthic habitat data from NOAA’s National Ocean Service (NOS) and the Florida Wildlife Research Institute (FWRI) provided validation data for the estimation of bottom type. Preliminary analyses of the bottom type estimation suggest that the best retrievals are for seagrass bottoms. One source of the potential difficulties may be that the LUT database was generated from a more pristine location (Lee Stocking Island, Bahamas). It is expected that fusing the HSI/LIDAR data streams should reduce the errors in bottom typing and IOP estimation.
Diver visibility analyses and predictions, and water transparency in general, are of significant military and commercial interest. This is especially true in our current state, where ports and harbors are vulnerable to terrorist attacks from a variety of platforms both on and below the water (swimmers, divers, AUVs, ships, submarines, etc.). Aircraft hyperspectral imagery has been previously used successfully to classify coastal bottom types and map bathymetry and it is time to transition this observational tool to harbor and port security. Hyperspectral imagery is ideally suited for monitoring small-scale features and processes in these optically complex waters, because of its enhanced spectral (1-3 nm) and spatial (1-3 meters) resolutions. Under an existing NOAA project (CICORE), a field experiment was carried out (November 2004) in coordination with airborne hyperspectral ocean color overflights to develop methods and models for relating hyperspectral remote sensing reflectances to water transparency and diver visibility in San Pedro and San Diego Bays. These bays were focused areas because: (1) San Pedro harbor, with its ports of Los Angeles and Long Beach, is the busiest port in the U.S. and ranks 3rd in the world and (2) San Diego Harbor is one of the largest Naval ports, serving a diverse mix of commercial, recreational and military traffic, including more than 190 cruise ships annual. Maintaining harbor and port security has added complexity for these Southern California bays, because of the close proximity to the Mexican border. We will present in situ optical data and hyperspectral aircraft ocean color imagery from these two bays and compare and contrast the differences and similarities. This preliminary data will then be used to discuss how water transparency and diver visibility predictions improve harbor and port security.
Bioluminescence emitted from marine organisms upon mechanical stimulation is an obvious military interest, as it provides a low-tech method of identifying surface and subsurface vehicles and swimmer tracks. Clearly, the development of a passive method of identifying hostile ships, submarines, and swimmers, as well as the development of strategies to reduce the risk of detection by hostile forces is relevant to Naval operations and homeland security. The measurement of bioluminescence in coastal waters has only recently received attention as the platforms and sensors were not scaled for the inherent small-scale nature of nearshore environments. In addition to marine forcing, many ports and harbors are influenced by freshwater inputs, differential density layering and higher turbidity. The spatial and temporal fluctuations of these optical water types overlaid on changes in the bioluminescence potential make these areas uniquely complex. The development of an autonomous underwater vehicle with a bioluminescence capability allows measurements on sub-centimeter horizontal and vertical scales in shallow waters and provides the means to map the potential for detection of moving surface or subsurface objects. A deployment in San Diego Bay shows the influence of tides on the distribution of optical water types and the distribution of bioluminescent organisms. Here, these data are combined to comment on the potential for threat reduction in ports and harbors.
Proc. SPIE. 3437, Infrared Spaceborne Remote Sensing VI
KEYWORDS: Data modeling, Calibration, Algorithm development, Atmospheric corrections, Coastal modeling, Space operations, Commercial off the shelf technology, Reflectivity, Atmospheric modeling, Imaging systems
A wide variety of applications of imaging spectrometry have been demonstrated using data from aircraft systems. Based on this experience the Navy is pursuing the Hyperspectral Remote Sensing Technology (HRST) Program to use hyperspectral imagery to characterize the littoral environment, for scientific and environmental studies and to meet Naval needs. To obtain the required space based hyperspectral imagery the Navy has joined in a partnership with industry to build and fly the Naval EarthMap Observer (NEMO). The NEMO spacecraft has the Coastal Ocean Imaging Spectrometer (COIS) a hyperspectral imager with adequate spectral and spatial resolution and a high signal-to- noise ratio to provide long term monitoring and real-time characterization of the coastal environment. It includes on- board processing for rapid data analysis and data compression, a large volume recorder, and high speed downlink to handle the required large volumes of data. This paper describes the algorithms for processing the COIS data to provide at-launch ocean data products and the research and modeling that are planned to use COIS data to advance our understanding of the dynamics of the coastal ocean.
The absorption of light by phytoplankton at a single wavelength, aph((lambda) ), is reduced with the increased packaging of the light absorption material. A common method of estimating the package effect is to divide aph((lambda) ) by the light absorption of the intracellular material after it has been extracted in an organic solvent. The absorption of the extract is often assumed to be representative of the true absorption of the cellular material in a dissolved state, asol((lambda) ). However, asol((lambda) ) is affected by the process of removing the light absorptive material from the organic matrix of the cell, the destruction of the pigment-protein complexes, and the solvent interference with the excited states of the chromophore. What is actually being measured by these extraction methods to determine asol((lambda) ), is aom((lambda) ), i.e., the absorption of light by the pigment material in the organic medium of the experiment. A solvation factor, S, that is the ratio of the true asol((lambda) ) to the measured aom((lambda) ) is needed before the package effect can be determined. We have developed an internally consistent measure of aph((lambda) ), aom(lambda), chlorophyll a concentration, and pheopigment concentration to determine the ratio asol((lambda) ):aom((lambda) ) and the package effect, Qa equals aph/at 675 nm. These parameters are used to determine a functional relationship between chlorophyll a concentrations and light absorption for high- light adapted, natural phytoplankton populations in optically clear waters. The packaging effect in these waters is negligible at the red peak of the spectrum. Exclusion of the weight specific absorption of pheopigments and the assumption of a zero aph(675) at a zero pigment concentration produces a misleading chlorophyll a-specific absorption and a false determination of pigment packaging. An algorithm is developed and validated for predicting chlorophyll a concentration from aph(675) in high-light, optically clear waters.