Optical modeling of ocean waters: Is the case 1 - case 2 classification still useful?

…two extreme cases can be identified and separated. Case 1 is that of a concentration of phytoplankton high compared to other particles…. In contrast, the inorganic particles are dominant in case 2.… In both cases dissolved yellow substance is present in variable amounts.… An ideal case 1 would be a pure culture of phytoplankton and an ideal case 2 a suspension of nonliving material with a zero concentration of pigments. Morel and Prieur emphasized that these ideal cases are not encountered in nature, and they suggested the use of high or low values of the ratio of pigment concentration to scattering coefficient as a basis for discriminating between Case 1 and Case 2 waters. Although no specific values of this ratio were proposed to serve as criteria for classification, their example data suggested that the ratio of chlorophyll a concentration (in mg m-3) to the scattering coefficient at 550 nm (in m-1) in Case 1 waters is greater than 1 and in Case 2 waters is less than 1. Importantly, however, Morel and Prieur also showed data classified as “intermediate waters” with the ratio between about 1 and 2.2. Although the original definition from 1977 did not imply a binary classification, the practice of most investigators in the following years clearly evolved toward a bipartite analysis

Detection of Seagrass Scars Using Sparse Coding and Morphological Filter

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 \u3e90% accuracy on the detection of seagrass scars

Black Phosphorus with Near-Superhydrophic Properties and Long-Term Stability in Aqueous Media

Black phosphorus is a two-dimensional material that has potential applications in energy storage, high frequency electronics and sensing, yet it suffers from instability in oxygenated and/or aqueous systems. Here we present the use of a polymeric stabilizer which prevents the degradation of nearly 68% of the material in aqueous media over the course of ca. 1 month

Ocean PHILLS hyperspectral imager : design, characterization, and calibration

The Ocean Portable Hyperspectral Imager for Low-Light Spectroscopy (Ocean PHILLS) is a hyperspectral imager specifically designed for imaging the coastal ocean. It uses a thinned, backside-illuminated CCD for high sensitivity and an all-reflective spectrograph with a convex grating in an Offner configuration to produce a nearly distortion-free image. The sensor, which was constructed entirely from commercially available components, has been successfully deployed during several oceanographic experiments in 1999–2001. Here we describe the instrument design and present the results of laboratory characterization and calibration. We also present examples of remote-sensing reflectance data obtained from the LEO-15 site in New Jersey that agrees well with ground-truth measurements.Keywords: Remote sensing, Ocean optics, Visible spectroscop

Satellite remote sensing data can be used to model marine microbial metabolite turnover

Sampling ecosystems, even at a local scale, at the temporal and spatial resolution necessary to capture natural variability in microbial communities are prohibitively expensive. We extrapolated marine surface microbial community structure and metabolic potential from 72 16S rRNA amplicon and 8 metagenomic observations using remotely sensed environmental parameters to create a system-scale model of marine microbial metabolism for 5904 grid cells (49 km2) in the Western English Chanel, across 3 years of weekly averages. Thirteen environmental variables predicted the relative abundance of 24 bacterial Orders and 1715 unique enzyme-encoding genes that encode turnover of 2893 metabolites. The genes’ predicted relative abundance was highly correlated (Pearson Correlation 0.72, P-value <10−6) with their observed relative abundance in sequenced metagenomes. Predictions of the relative turnover (synthesis or consumption) of CO2 were significantly correlated with observed surface CO2 fugacity. The spatial and temporal variation in the predicted relative abundances of genes coding for cyanase, carbon monoxide and malate dehydrogenase were investigated along with the predicted inter-annual variation in relative consumption or production of ~3000 metabolites forming six significant temporal clusters. These spatiotemporal distributions could possibly be explained by the co-occurrence of anaerobic and aerobic metabolisms associated with localized plankton blooms or sediment resuspension, which facilitate the presence of anaerobic micro-niches. This predictive model provides a general framework for focusing future sampling and experimental design to relate biogeochemical turnover to microbial ecology

Adaptive Sampling in the Coastal Ocean at the Long Ecosystem Observatory

A multi-platform inter-disciplinary observation network has been operating at the Rutgers University Long-term Ecosystem Observatory (LEO) since 1996, with real-time capabilities beginning in July 1999. The network integrates numerous remote (satellites and shore- based), stationary (surface and subsurface), moveable (ships and AUVs) observation systems. The observation network provides spatially extensive updates of the physics, optics, chemistry and biology on time scales of an hour or less which are communicated in real-time to shipboard scientists and AUV operators. This rapid environmental assessment capability is already changing current paradigms for ocean adaptive sampling strategies. For example, in the well-sampled ocean, where forecast errors are dominated by uncertainties in the model physics or future boundary conditions rather than initial conditions, ensemble forecasts with differing model parameterizations can be used to identify regions in which additional data can be used to keep a model on track. Furthermore these approaches are key for sampling episodic events that play a disproportionately large role in driving the biogeochemistry of the coastal ocean. Results from the 1999 and 2000 seasons demonstrate the usefulness of the LEO network to identify, track and sample small-scale (10 m) features that would otherwise go unnoticed with traditional sampling approaches. For example, during HyCODE 2000, real-time data from physical survey vessels identified offshore convergence features. These observations combined with the surface current CODAR measurements were used to adjust and successfully maneuver other ships that were outfitted with bio-optical instrumentation. Bioluminescent Ceratium fusus, a red-tide dinoflagellate, were observed in the convergence zone. The covergence zone resulted from material collecting against a southward flowing alongshore jet of low saline water and tidally driven onshore flow of offshore waters. The feature was dramatically impacted by tidal forcing with convergence at high tide and dispersion at low tide

Optical Monitoring and Forecasting Systems for Harmful Algal Blooms: Possibility or Pipe Dream?

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

Bioinformatic Approaches For Objective Detection of Water Masses on Continental Shelves

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

The Effects of Temporal Variability of Mixed Layer Depth on Primary Productivity Around Bermuda

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°‐34°N, 62°‐64°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