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Seagrasses provide a number of critical ecosystem services, including habitat for numerous species, sediment stabilization, and shoreline protection. Ariel photography is a useful tool to estimate the areal extent of seagrasses, but recent innovations in radiometrically calibrated sensors and algorithm development have allowed identification of benthic types and retrieval of absolute density. This study demonstrates the quantitative ability of a high spatial resolution (1 m) airborne hyperspectral sensor (3.2 nm bandwidth) in the complex coastal waters of Saint Joseph’s Bay (SJB). Several benthic types were distinguished, including submerged and floating aquatic vegetation, benthic red algae, bare sand, and optically deep water. A total of 23.6 km2 of benthic vegetation was detected, indicating no dramatic change in vegetation area over the past 30 years. SJB supported high seagrass density at depths shallower than 2 m with an average leaf area index of 2.0 ± 0.6 m2 m−2. Annual seagrass production in the bay was 13,570 t C year−1 and represented 41 % of total marine primary production. The effects of coarser spatial resolution were investigated and found to reduce biomass retrievals, underestimate productivity, and alter patch size statistics. Although data requirements for this approach are considerable, water column optical modeling may reduce the in situ requirements and facilitate the transition of this technique to routine monitoring efforts. The ability to quantify not just areal extent but also productivity of a seagrass meadow in optically complex coastal waters can provide information on the capacity of these environments to support marine food webs.

A primary limitation of phytoplankton ecology research is the difficulty of describing patchiness and distributions of different phytoplankton groups. Chlorophyll fluorescence and optical backscatter are useful measurements that provide information about phytoplankton, but these measurements do not allow distinction of phytoplankton taxa. Traditional phytoplankton identification methods (such as microscopy, HPLC analysis, and flow cytometry) are labor intensive and therefore can provide only very limited coverage and resolution. Through lab experiments we show that the Laser In Situ Scattering and Transmissometer (LISST-100) instrument can accurately quantify phytoplankton cell dimensions for some cell shapes. Pseudo-spherical dinoflagellates are described with a single peak in the particle size distribution (PSD) at the cross-sectional dimension of the cells. Pennate diatoms are described with peaks in the PSD at the major and minor axis dimensions of the cells. Diatom cells with minor axis dimensions that vary along the major axis are described with one peak across the range of minor axis dimensions and a second peak at the major axis dimension. Through field experiments, we show that mapping the PSD in situ at high resolution permits description of patchiness and evolution of phytoplankton populations. We present two examples: (1) growing dominance of Ceratium species during a red tide bloom, and (2) concentration of Pseudo-nitzschia australis, a harmful algal bloom (HAB) species, at a water mass front. We conclude that synoptic mapping of the PSD can significantly advance phytoplankton ecology research in the coastal ocean.

This is the publisher’s final pdf. The published article is copyrighted by Oceanography Society and can be found at: http://www.tos.org/.

This is the publisher’s final pdf. The published article is copyrighted by The Oceanography Society and can be found at: http://www.tos.org/.

This is the publisher’s final pdf. The published article is copyrighted by Oceanography Society and can be found at: http://www.tos.org/.

Bioluminescence (BL) predictability experiments (predictions of the intensity, depth, and distance offshore of the BL maximum) were conducted using an advective–diffusive tracer model with velocities and diffusivities from a fine-resolution model of the Monterey Bay, California, area. For tracer initialization, observations were assimilated into the tracer model while velocities and diffusivities were taken from the hydrodynamic model and kept unchanged during the initialization process. This dynamic initialization procedure provides an equilibrium tracer distribution that is balanced with the velocity and diffusivity fields from the hydrodynamic model. This equilibrium BL distribution was used as the initial BL field for 3 days of prognostic calculations. Two cross-shore surveys of bioluminescence data conducted at two locations (north of the bay and inside the bay) were used in four numerical experiments designed to estimate the limits of bioluminescence predictions by tracers. The cross-shore sections extended to around 25 km offshore, they were around 30 m deep, and on average they were approximately 35 km apart from each other. Bioluminescence predictability experiments demonstrated a strong utility of the tracer model (combined with limited bioluminescence observations and with the output from a circulation model) in predicting (over a 72-h period and over 25–35-km distances) the location and intensity of the BL maximum. Analysis of the model velocity fields and observed and model-predicted bioluminesence fields shows that the BL maximum is located in the frontal area representing a strong reversal of flow direction.

A simple ecological model, coupled to a primitive equation circulation model, is able to replicate the observed alongshore transport of the toxic dinoflagellate Karenia brevis on the West Florida shelf during a fall red tide in 1979. Initial land fall of these populations at the coast in our model matches shoreline data sets as well. The simulated vertical movement of K. brevis, in response to light-cued migration and nocturnal mixing, also mimics these aspects of the next fall red tide in 1980, suggesting that sunrise populations may provide the strongest surface signal, for detection of red tides by remote sensors aboard aircraft and satellites. Once a mature red tide is formed, a light-regulated maximal growth rate of 0.15 day−1, reflecting nutrient-limitation, and no other loss processes may be an adequate description of population dynamics above the 30–40 m isobaths, where blooms of K. brevis originate. Within shallow waters at the 10-m isobath, however, an apparent larger growth rate of 0.80 day−1—as a presumed consequence of frontal aggregations—must be offset by unknown processes of algal mortality. Likely candidates for cumulative, biomass-dependent losses are UV-B irradiation, microbial-induced lysis, and unselective grazing pressure from copepods, protozoans and heterotrophic dinoflagellates.

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

We have thus far constructed a series of coupled ecological/physical models to predict the origin and fate of harmful algal blooms of the toxic dinoflagellate, Gymnodinium breve, on the West Florida shelf. We find that (1) the maximal population growth rate of G. breve must be ∼0.80 day−1 during initiation of a red tide, but as little as 0.08 day−1 during its decay, (2) diatoms dominate when estuarine and shelf-break supplies of nitrate are made available to a model community of small and large diatoms, coccoid cyanophytes and Trichodesmium, non-toxic and red-tide dinoflagellates, microflagellates, and coccolithophores, (3) a numerical recipe for large red tides of G. breve requires DON supplies, mediated by iron-starved, nitrogen-fixers, while small blooms may persist on sediment sources of DON, (4) selective grazing must be exerted on the non-toxic dinoflagellates, and (5) vertical migration of G. breve in relation to seasonal changes of summer downwelling and fall/winter upwelling flow fields determine...

water is nearly opaque to infrared energy (Figure 1),the study of ocean optics has been primarily concernedwith propagation of visible energy, i.e. light. This is alsoa natural starting point for the study of ocean ecologyas photosynthesis is driven (with a few exceptions) byenergy within the visible light spectrum, which has suf-ficient energy per photon to induce photochemistry. Asphytoplankton accumulations also impact the colorand clarity of the water column, there is a direct linkbetween the studies of ocean optics and ocean ecology(Yentsch and Phinney, 1989).

Monitoring programs for harmful algal blooms (HABs) are currently reactive and provide little or no means for advance warning. Given this, the development of algal forecasting systems would be of great use because they could guide traditional monitoring programs and provide a proactive means for responding to HABs. Forecasting systems will require near real-time observational capabilities and hydrodynamic/biological models designed to run in the forecast mode. These observational networks must detect and forecast over ecologically relevant spatial/ temporal scales. One solution is to incorporate a multiplatform optical approach utilizing remote sensing and in situ moored technologies. Recent advances in instrumentation and data-assimilative modeling may provide the components necessary for building an algal forecasting system. This review will outline the utility and hurdles of optical approaches in HAB detection and monitoring. In all the approaches, the desired HAB information must be isolated and extracted from the measured bulk optical signals. Examples of strengths and weaknesses of the current approaches to deconvolve the bulk optical properties are illustrated. After the phytoplankton signal has been isolated, species-recognition algorithms will be required, and we demonstrate one approach developed for Gymnodinium breve Davis. Pattern-recognition algorithms will be species-specific, reflecting the acclimation state of the HAB species of interest.Field data will provide inputs to optically based ecosystem models, which are fused to the observational networks through data-assimilation methods. Potential model structure and data-assimilation methods are reviewed.

Coupled biological-physical models of carbon-nitrogen cycling by phytoplankton, zooplankton, and bacteria assess the impacts of nitrogen fixation and upwelled nitrate during new production within the shelf environs of the Cariaco Basin. During spring upwelling in response to a mean wind forcing of 8 m s−1, the physical model matches remote-sensing and hydrographic estimates of surface temperature. Within the three-dimensional flow field, the steady solutions of the biological model of a simple food web of diatoms, adult calanoid copepods, and ammonifying/nitrifying bacteria approximate within ∼9% the mean spring observations of settling fluxes caught by a sediment trap at ∼240 m, moored at our time series site in the basin. The models also estimate within ∼11% the average 14C net primary production and mimic the sparse observations of the spatial fields of nitrate and light penetration during the same time period of February-April. Stocks of colored dissolved organic matter are evidently small and diazotrophy is minimal during spring. In one summer case of the model with weaker wind forcing, however, the simulated net primary production is 14% of that measured in August-September, while the predicted detrital flux is then 30% of the observed. Addition of a cyanophyte state variable, with another source of new nitrogen, would remedy the seasonal deficiencies of the biological model, attributed to use of a single phytoplankton group.

The absorption of light by particles at a single wavelength, a Ph (λ), is reduced with increased packaging of the light absorption material within these particles. This reduction can be described by the parameter Q α * : Formula chim. where a sol (λ) is the theoretical maximum light absorption of the cellular material, a cm , in a completely dissolved state. In practice, the estimations a sol (λ) for living phytoplankton are hampered by the process of removing the chloroplast/thylakoid membranes from the organic matrix of the cell, the destruction of the pigment-protein complexes when an organic solvent is used, and the interference of the excited states of the pigment chromophore by the organic solvent. What is actually being measured by many of the current methods trying to determine a sol (λ) is a om (λ), i.e. the absorption of light by the pigment material in the organic medium of the experiment (methanol, acetone, Triton-X, etc.). The solvation factor, S, in the above equation is the ratio of the true a sol (λ) to the measured a om (λ). We have developed an internally consistent measure of a Ph (λ), a om (λ), Chl a concentration, and pheopigment concentration to determine the value of Q α * X S. This relationship is used to determine a functional relationship for Chl a absorption for high-light-adapted, natural phytoplankton populations in optically clear waters. The nonlinear packaging effect in these waters is insignificant at the red end of the spectrum. Exclusion of the weight-specific absorption of pheopigments and the assumption of a zero a ph (λ) at a zero pigment (Chl a + pheopigment) concentration produces a misleading Chl a-specific absorption and a false determination of pigment packaging. An algorithm is developed for predicting Chl a concentration from a ph (675).

The calibration of multispectral and hyperspectral imaging systems is typically done in the laboratory using an integrating sphere, which usually produces a signal that is red rich. Using such a source to calibrate environmental monitoring systems presents some difficulties. Not only is much of the calibration data outside the range and spectral quality of data values that are expected to be captured in the field, using these measurements alone may exaggerate the optical flaws found within the system. Left unaccounted for, these flaws will become embedded in to the calibration, and thus, they will be passed on to the field data when the calibration is applied. To address these issues, we used a series of well-characterized spectral filters within our calibration. It provided us with a set us stable spectral standards to test and account for inadequacies in the spectral and radiometric integrity of the optical imager.

[1] Novel remote sensing methods and in situ observations reveal that intense dinoflagellate blooms occur frequently in Monterey Bay, California. Blooms can contain surface chlorophyll concentrations exceeding 500 μg l−1 and occupy ∼5 to 80 km2. They occur primarily during August through November and can persist for > 1 month. Maximum bloom frequency and mean intensity are in a shallow (< 25 m depth) area of the northeastern bay, in coincidence with the warmest surface water, low wind stress, and retentive circulation. These conditions favor dinoflagellates, which can vertically migrate to acquire nutrients in the thermocline and aggregate as "red tide" near the surface. Bloom incubation areas, also indicated in other coastal upwelling systems, may disproportionately influence regional bloom ecology.

[1] Nighttime water-leaving radiance is a function of the depth-dependent distribution of both the in situ bioluminescence emissions and the absorption and scattering properties of the water. The vertical distributions of these parameters were used as inputs for a modified one-dimensional radiative transfer model to solve for spectral bioluminescence water-leaving radiance from prescribed depths of the water column. Variation in the water-leaving radiance was consistent with local episodic physical forcing events, with tidal forcing, terrestrial runoff, particulate accumulation, and biological responses influencing the shorter timescale dynamics. There was a >90 nm shift in the peak water-leaving radiance from blue (∼474 nm) to green as light propagated to the surface. In addition to clues in ecosystem responses to physical forcing, the temporal dynamics in intensity and spectral quality of water-leaving radiance provide suitable ranges for assessing detection. This may provide the information needed to estimate the depth of internal light sources in the ocean, which is discussed in part 2 of this paper.

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.

Paper details the construction of a new hyperspectral sensor focused on the coastal environment. This sensor follows the same basic design strategy as its predecessor, the NRL developed PHILLS sensor.

The Coastal Ocean Applications and Science Team (COAST) conducted its first experiment imaging a Harmful Algal Bloom (HAB) In Monterey Bay, CA. We describe the hyperspectral imagery and the detection of the HAB.

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.

[1] Recent advances in hydrologic optics offer the potential for quantitative maps of inherent optical properties, which can be inverted into optically significant constituents. During summer experiments in the Mid-Atlantic Bight (MAB) a procedure to invert bulk absorption measurements from off-the-shelf technology was developed. The inversion provides optical concentration estimates of phytoplankton, colored dissolved organic matter (CDOM), and detritus. Inversion estimates were validated against chlorophyll fluorescence, filter pad absorption, and phytoplankton pigment measurements. The inversion could account for up to 90% of the observed variance in particulates, CDOM, and detritus. Robust estimates for phytoplankton community composition could be achieved but required constraints on the inversion that phytoplankton dominate the red light absorption. Estimates for the composition, as indicated by spectral slopes, for CDOM and detritus were not robust. During the summer months in nearshore waters of the MAB, total absorption was almost equally associated (±10%) with phytoplankton, detritus, and CDOM, and the regions of variability were associated with major frontal boundaries. The variance between particulates, CDOM, and detritus varied spatially and with year; which precluded robust correlations.

[1] As part of the 2001 Hyper Spectral Coupled Ocean Dynamics Experiment, sea surface temperature and ocean color satellite imagery were collected for the continental shelf of the Mid-Atlantic Bight. These images were used to develop a water mass analysis and classification scheme that objectively describes the locations of water masses and their boundary locations. This technique combines multivariate cluster analysis with a newly developed genetic expression algorithm to objectively determine the number of water types in the region on the basis of ocean color and sea surface temperature measurements. Then, through boundary analysis of the water types identified, the boundaries of the major water types were mapped and the differences between them were quantified using predictor space distances. Results suggest that this approach can track the development and transport of water masses. Because the analysis combines the information of multiple predictors to describe water masses, it is an effective tool in detecting water masses not readily recognizable with temperature or chlorophyll alone.

Publisher Summary This chapter describes the basics of ocean color remote sensing. It includes a description to obtain and use SeaWiFS data within NASA's freely available ocean color remote sensing software. Differences in methodology and some of the more recent developments in the optical remote sensing field are described. By exploring how light penetrates the water column and how the optical constituents affects the light as it travels through the water, the basic understanding of the value and limitations of ocean color data is provided. Remote sensing provides a tool that can provide information over time/space scales not possible using traditional sampling approaches from ships. SeaWiFS data can be acquired from NASA's Distributed Active Archive Center [DAAC], and can be ordered online. SeaWiFS is a commercial instrument flying on Orbimage's Orbview-2 spacecraft. Apart from the other image processing packages available, the chapter describes SeaDAS, as it is free and can be used with a currently operational satellite sensor (SeaWiFS). There are many other ocean color satellites being planned (and one that has just been launched), but the data streams are not currently available.

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.© (1998) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

The optical variations observed within ship wakes are largely due to the generation of copious amounts of air bubbles in the upper ocean, a fraction of which accumulate as foam at the surface, where they release scavenged surfactants. Field experiments were conducted to test previous theoretical predictions of the variations in optical properties that result from bubble injection in the surface ocean. Variations in remote-sensing reflectance and size distribution of bubbles within the ship-wake zone were determined in three different optical water types: the clear equatorial Pacific Ocean, moderately turbid coastal waters, and very turbid coastal waters, the latter two of which were offshore of New Jersey. Bubbles introduced by moving vessels increased the backscattering in all cases, which in turn enhanced the reflectance over the entire visible and infrared wave bands. The elevated reflectance had different spectral characteristics in the three locations. The color of ship wakes appears greener in the open ocean, whereas little change in color was observed in near-coastal turbid waters, consistent with predictions. Colorless themselves, bubbles increase the reflected radiance and change the color of the ocean in a way that depends on the spectral backscattering and absorption of the undisturbed background waters. For remote observation from aircraft or satellite, the foam and added surfactants further enhance the reflectance to a degree dependent on the illumination and the viewing geometry.

Temporal variations in primary production and surface chlorophyll concentrations, as measured by ship and satellite around Bermuda, were simulated with a numerical model. In the upper 450 m of the water column, population dynamics of a size-fractionated phytoplankton community were forced by daily changes of wind, light, grazing stress, and nutrient availability. The temporal variations of production and chlorophyll were driven by changes in nutrient introduction to the euphotic zone due to both high- and low-frequency changes of the mixed layer depth within 32 deg-34 deg N, 62 deg-64 deg W between 1979 and 1984. Results from the model derived from high-frequency (case 1) changes in the mixed layer depth showed variations in primary production and peak chlorophyll concentrations when compared with results from the model derived from low-frequency (case 2) mixed layer depth changes. Incorporation of size-fractionated plankton state variables in the model led to greater seasonal resolution of measured primary production and vertical chlorophyll profiles. The findings of this study highlight the possible inadequacy of estimating primary production in the sea from data of low-frequency temporal resolution and oversimplified biological simulations.