The Long-term Ecosystem Observatory: an integrated coastal observatory

Schofield, Oscar; Bergmann, Trisha; Bissett, Paul; Grassle, J. Frederick; Haidvogel, Dale B.; Kohút, J.; Moline, Mark A.; Glenn, Scott

doi:10.1109/joe.2002.1002469

Cited by 96 publications

(61 citation statements)

References 21 publications

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“…The 2000 Hyperspectral Coupled Ocean Dynamics Experiment (HyCODE) conducted at LEO represents an operational integrated coastal-ocean-observing network [Glenn et al, 2000;Schofield et al, 2002]. As part of this experiment, in-water physical and bio-optical time series data were collected from two profiling instrument nodes linked to shore via an electro-optical cable.…”

Section: Methodsmentioning

confidence: 99%

“…These upwelling-favorable events result in phytoplankton blooms [Schofield et al, 2002] and represent one of the dominant biogeochemical signals in nearshore (<30 m depth) New Jersey coastal waters (Glenn et al, submitted manuscript, 2003). However, the optical properties in the region are complex due to the proximity of the Mullica River estuary (Figure 1) and the presence of coastally trapped freshwater plumes from the Hudson River, which introduce significant amounts of CDOM and detritus.…”

Section: Physical and Optical Properties Of The Study Sitementioning

confidence: 99%

“…Here we present a high-resolution time series of in-water physical and optical data collected by two cabled profilers as part of the Long-term Ecosystem Observatory (LEO) [see Schofield et al, 2002] to demonstrate an approach which can potentially ''fill the gap'' between satellite-based depth-integrated productivity models and productivity models dependent on discrete water samples such as wavelength-resolved models. From this time series we directly derive the spectral absorption of phytoplankton in coastal waters from bulk optical parameters measured with ''off the shelf'' technology and quantify its utility in bio-optically estimating primary productivity in coastal waters.…”

Section: Introductionmentioning

confidence: 99%

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Deriving in situ phytoplankton absorption for bio‐optical productivity models in turbid waters

et al. 2004

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As part of Hyperspectral Coupled Ocean Dynamics Experiment, a high-resolution hydrographic and bio-optical data set was collected from two cabled profilers at the Long-Term Ecosystem Observatory (LEO). Upwelling-and downwelling-favorable winds and a buoyant plume from the Hudson River induced large changes in hydrographic and optical structure of the water column. An absorption inversion model estimated the relative abundance of phytoplankton, colored dissolved organic matter (CDOM) and detritus, as well as the spectral exponential slopes of CDOM and detritus from in situ WET Labs nine-wavelength absorption/attenuation meter (ac-9) absorption data. Derived optical weights were proportional to the parameter concentrations and allowed for their absorptions to be calculated. Spectrally weighted phytoplankton absorption was estimated using modeled spectral irradiances and the phytoplankton absorption spectra inverted from an ac-9. Derived mean spectral absorption of phytoplankton was used in a bio-optical model estimating photosynthetic rates. Measured radiocarbon uptake productivity rates extrapolated with water mass analysis and the bio-optical modeled results agreed within 20%. This approach is impacted by variability in the maximum quantum yield (Ф) and the irradiance light-saturation parameter (Ek (PAR)). An analysis of available data shows that Фmax variability is relatively constrained in temperate waters. The variability of Ek(PAR) is greater in temperate waters, but based on a sensitivity analysis, has an overall smaller impact on water-column-integrated productivity rates because of the exponential decay of light. This inversion approach illustrates the utility of bio-optical models in turbid coastal waters given the measurements of the bulk inherent optical properties.

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Section: Methodsmentioning

confidence: 99%

Section: Physical and Optical Properties Of The Study Sitementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Deriving in situ phytoplankton absorption for bio‐optical productivity models in turbid waters

et al. 2004

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“…Seagliders were sent on five-month-long missions to measure physics, biology and chemistry off the coast of Washington and in the Labrador Sea [15]. Slocum gliders were introduced as an integral part of the Long-term Ecosystem Observatory (LEO), an integrated observatory off the coast of New Jersey [24,25,26].…”

Section: Background and Historymentioning

confidence: 99%

Cooperative Vehicle Environmental Monitoring

Leonard

2016

Springer Handbook of Ocean Engineering

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Over the last decade, methodologies for automated cooperative control of robotic vehicles have been designed, deployed and proven to provide efficient, reliable, and sustained monitoring of the uncertain and inhospitable ocean environment. Unprecedented data sets have been collected from deployments of cooperative vehicles in the field, and both real-time and post-deployment analyses have led to new understanding of the environment. This first decade of success in cooperative vehicle environmental monitoring sets the stage for new opportunities and future gain, especially as the development of cooperative control methodologies can continue to leverage ongoing technological and scientific advances in underwater communication and sensing, energy and computational efficiency, vehicle size, speed, maneuverability and cost, and ocean modeling and prediction.Indeed, the demonstrated potential of cooperative vehicle control has led to increased demand for fleets of autonomous underwater vehicles (AUVs) for use in measuring ocean physics, biology, chemistry and geology to improve understanding of natural dynamics and human-influenced changes in the marine environment. Further, methodologies for cooperative control of robotic vehicles in the ocean are readily adaptable to applications on land, in the air and in space; likewise, there is much to be learned from developments in these other domains. The recent explosion in research on networks and complex systems, including investigation of mechanisms that explain a "collective intelligence" exhibited by animal aggregations on the move, are also being leveraged to advance design of cooperative vehicle dynamics.For environmental monitoring to be successful, physical, chemical, and biological variables must be measured across a range of spatial and temporal scales; in the ocean the monitoring strategy must also contend with a harsh, three-dimensional physical space that is highly uncertain and dynamic. Small spatial and temporal scales associated with the measured variables typically make a stationary sensor array impractical because a very large number of sensors would be needed to get sufficient resolution in space and/or time. An array of mobile sensors, however, may be very well suited to such a challenge since mobility can be exploited to dynamically distribute fewer sensors according to the spatial and temporal scales.The underlying principle of cooperative control of vehicles for environmental monitoring leverages mobility of sensors and uses an interacting dynamic among the individual sensors to yield a collective behavior that performs better than the sum of the parts. If the vehicles can communicate their state or measure the relative state of others in the team, then they can cooperate and the cooperative vehicle dynamics can provide coordinated motion of the team as a whole. The resulting vehicle network functions as a dynamically reconfigurable sensor array with a capability for high performance in environmental monitoring not available at the level of individual...

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“…The important role of ocean observatories in future oceanography has received international acknowledgement, and initial projects have emerged around the world (e.g., Forrester et al, 1997;Momma et al, 1998;Beranzoli et al, 1998;Glenn et al, 2000;Delaney et al, 2000;Chave et al, 2002;Schofield et al, 2002;Hirata et al, 2002;Austin et al, 2002;Petitt et al, 2002;Beranzoli et al, 2003). Major ocean observatory infrastructure projects have been funded in Canada (NEPTUNE Canada), and are under consideration in Japan (Advanced Real-Time Earth monitoring Network in the Area or ARENA; Shirasaki et al, 2003), Europe (The European Seafloor Observatory Network or ESONET; http://www.abdn.ac.uk/ecosystem/esonet/), and the United States (Ocean Research Interactive Observatory Networks or ORION; http://www.coreocean.org/Dev2Go.web?id=251294&rnd=3212).…”

Section: Introductionmentioning

confidence: 99%

Cabled Ocean Observatory Systems

Chave¹,

Waterworth²,

Maffei³

et al. 2004

mar technol soc j

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Future studies of episodic processes in the ocean and earth will require new tools to complement traditional, ship-based, expeditionary science. This will be enabled through the construction of innovative facilities called ocean observatories which provide unprecedented amounts of power and two-way bandwidth to access and control instrument networks in the oceans. The most capable ocean observatories are designed around a submarine fiber optic/power cable connecting one or more seafloor science nodes to the terrestrial power grid and communications backhaul. This paper defines the top level requirements that drive cabled observatory design and the system engineering environment within which a scientifically-capable infrastructure can be implemented. Commercial high reliability submarine telecommunication technologies which will be crucial in the design of long term cabled observatories are then reviewed. The top level architecture of a generic cabled observatory, describing the main subsystems comprising the whole and defining technological approaches to their engineering, is then described, along with some example design choices and tradeoff studies.

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The Long-term Ecosystem Observatory: an integrated coastal observatory

Cited by 96 publications

References 21 publications

Deriving in situ phytoplankton absorption for bio‐optical productivity models in turbid waters

Deriving in situ phytoplankton absorption for bio‐optical productivity models in turbid waters

Cooperative Vehicle Environmental Monitoring

Cabled Ocean Observatory Systems

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