Satellite sensor requirements for monitoring essential biodiversity variables of coastal ecosystems.

The biodiversity and high productivity of coastal terrestrial and aquatic habitats are the foundation for important benefits to human societies around the world. These globally distributed habitats need frequent and broad systematic assessments, but field surveys only cover a small fraction of these areas. Satellite-based sensors can repeatedly record the visible and near-infrared reflectance spectra that contain the absorption, scattering, and fluorescence signatures of functional phytoplankton groups, colored dissolved matter, and particulate matter near the surface ocean, and of biologically structured habitats (floating and emergent vegetation, benthic habitats like coral, seagrass, and algae). These measures can be incorporated into Essential Biodiversity Variables (EBVs), including the distribution, abundance, and traits of groups of species populations, and used to evaluate habitat fragmentation. However, current and planned satellites are not designed to observe the EBVs that change rapidly with extreme tides, salinity, temperatures, storms, pollution, or physical habitat destruction over scales relevant to human activity. Making these observations requires a new generation of satellite sensors able to sample with these combined characteristics: (1) spatial resolution on the order of 30 to 100-m pixels or smaller; (2) spectral resolution on the order of 5 nm in the visible and 10 nm in the short-wave infrared spectrum (or at least two or more bands at 1,030, 1,240, 1,630, 2,125, and/or 2,260 nm) for atmospheric correction and aquatic and vegetation assessments; (3) radiometric quality with signal to noise ratios (SNR) above 800 (relative to signal levels typical of the open ocean), 14-bit digitization, absolute radiometric calibration <2%, relative calibration of 0.2%, polarization sensitivity <1%, high radiometric stability and linearity, and operations designed to minimize sunglint; and (4) temporal resolution of hours to days. We refer to these combined specifications as H4 imaging. Enabling H4 imaging is vital for the conservation and management of global biodiversity and ecosystem services, including food provisioning and water security. An agile satellite in a 3-d repeat low-Earth orbit could sample 30-km swath images of several hundred coastal habitats daily. Nine H4 satellites would provide weekly coverage of global coastal zones. Such satellite constellations are now feasible and are used in various applications

Satellite sensor requirements for monitoring essential biodiversity variables of coastal ecosystems

© The Author(s), 2018. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Ecological Applications 28 (2018): 749-760, doi: 10.1002/eap.1682.The biodiversity and high productivity of coastal terrestrial and aquatic habitats are the foundation for important benefits to human societies around the world. These globally distributed habitats need frequent and broad systematic assessments, but field surveys only cover a small fraction of these areas. Satellite‐based sensors can repeatedly record the visible and near‐infrared reflectance spectra that contain the absorption, scattering, and fluorescence signatures of functional phytoplankton groups, colored dissolved matter, and particulate matter near the surface ocean, and of biologically structured habitats (floating and emergent vegetation, benthic habitats like coral, seagrass, and algae). These measures can be incorporated into Essential Biodiversity Variables (EBVs), including the distribution, abundance, and traits of groups of species populations, and used to evaluate habitat fragmentation. However, current and planned satellites are not designed to observe the EBVs that change rapidly with extreme tides, salinity, temperatures, storms, pollution, or physical habitat destruction over scales relevant to human activity. Making these observations requires a new generation of satellite sensors able to sample with these combined characteristics: (1) spatial resolution on the order of 30 to 100‐m pixels or smaller; (2) spectral resolution on the order of 5 nm in the visible and 10 nm in the short‐wave infrared spectrum (or at least two or more bands at 1,030, 1,240, 1,630, 2,125, and/or 2,260 nm) for atmospheric correction and aquatic and vegetation assessments; (3) radiometric quality with signal to noise ratios (SNR) above 800 (relative to signal levels typical of the open ocean), 14‐bit digitization, absolute radiometric calibration <2%, relative calibration of 0.2%, polarization sensitivity <1%, high radiometric stability and linearity, and operations designed to minimize sunglint; and (4) temporal resolution of hours to days. We refer to these combined specifications as H4 imaging. Enabling H4 imaging is vital for the conservation and management of global biodiversity and ecosystem services, including food provisioning and water security. An agile satellite in a 3‐d repeat low‐Earth orbit could sample 30‐km swath images of several hundred coastal habitats daily. Nine H4 satellites would provide weekly coverage of global coastal zones. Such satellite constellations are now feasible and are used in various applications.National Center for Ecological Analysis and Synthesis (NCEAS); National Aeronautics and Space Administration (NASA) Grant Numbers: NNX16AQ34G, NNX14AR62A; National Ocean Partnership Program; NOAA US Integrated Ocean Observing System/IOOS Program Office; Bureau of Ocean and Energy Management Ecosystem Studies program (BOEM) Grant Number: MC15AC0000

Satellite Sensor Requirements for Monitoring Essential Biodiversity Variables of Coastal Ecosystems

The biodiversity and high productivity of coastal terrestrial and aquatic habitats are the foundation for important benefits to human societies around the world. These globally distributed habitats need frequent and broad systematic assessments, but field surveys only cover a small fraction of these areas. Satellite-based sensors can repeatedly record the visible and near-infrared reflectance spectra that contain the absorption, scattering, and fluorescence signatures of functional phytoplankton groups, colored dissolved matter, and particulate matter near the surface ocean, and of biologically structured habitats (floating and emergent vegetation, benthic habitats like coral, seagrass, and algae). These measures can be incorporated into Essential Biodiversity Variables (EBVs), including the distribution, abundance, and traits of groups of species populations, and used to evaluate habitat fragmentation. However, current and planned satellites are not designed to observe the EBVs that change rapidly with extreme tides, salinity, temperatures, storms, pollution, or physical habitat destruction over scales relevant to human activity. Making these observations requires a new generation of satellite sensors able to sample with these combined characteristics: (1) spatial resolution on the order of 30 to 100-m pixels or smaller; (2) spectral resolution on the order of 5 nm in the visible and 10 nm in the short-wave infrared spectrum (or at least two or more bands at 1,030, 1,240, 1,630, 2,125, and/or 2,260 nm) for atmospheric correction and aquatic and vegetation assessments; (3) radiometric quality with signal to noise ratios (SNR) above 800 (relative to signal levels typical of the open ocean), 14-bit digitization, absolute radiometric calibratio

Dispersal Routes and Habitat Utilization of Juvenile Atlantic Bluefin Tuna, Thunnus thynnus, Tracked with Mini PSAT and Archival Tags

Between 2005 and 2009, we deployed 58 miniature pop-up satellite archival tags (PSAT) and 132 implanted archival tags on juvenile Atlantic bluefin tuna (age 2–5) in the northwest Atlantic Ocean. Data returned from these efforts (n = 26 PSATs, 1 archival tag) revealed their dispersal routes, horizontal and vertical movements and habitat utilization. All of the tagged bluefin tuna remained in the northwest Atlantic for the duration observed, and in summer months exhibited core-use of coastal seas extending from Maryland to Cape Cod, MA, (USA) out to the shelf break. Their winter distributions were more spatially disaggregated, ranging south to the South Atlantic Bight, northern Bahamas and Gulf Stream. Vertical habitat patterns showed that juvenile bluefin tuna mainly occupied shallow depths (mean  = 5–12 m, sd  = 15–23.7 m) and relatively warm water masses in summer (mean  = 17.9–20.9°C, sd  = 4.2–2.6°C) and had deeper and more variable depth patterns in winter (mean  = 41–58 m, sd  = 48.9–62.2 m). Our tagging results reveal annual dispersal patterns, behavior and oceanographic associations of juvenile Atlantic bluefin tuna that were only surmised in earlier studies. Fishery independent profiling from electronic tagging also provide spatially and temporally explicit information for evaluating dispersals rates, population structure and fisheries catch patterns

On the modeling of the 2010 Gulf of Mexico Oil Spill

► Two oil particle trajectory forecasting systems for the 2010 Deepwater Horizon Oil Spill in the Gulf of Mexico are presented. ► A Monte Carlo method was used to model oil removal processes. ► Results were sensitive to initial conditions. ► Data-assimilative models produced the most accurate trajectories. ► About 25% of the oil remains in the water column and most of the oil is below 800 m after three months of simulation.Two oil particle trajectory forecasting systems were developed and applied to the 2010 Deepwater Horizon Oil Spill in the Gulf of Mexico. Both systems use ocean current fields from high-resolution numerical ocean circulation model simulations, Lagrangian stochastic models to represent unresolved sub-grid scale variability to advect oil particles, and Monte Carlo-based schemes for representing uncertain biochemical and physical processes. The first system assumes two-dimensional particle motion at the ocean surface, the oil is in one state, and the particle removal is modeled as a Monte Carlo process parameterized by a one number removal rate. Oil particles are seeded using both initial conditions based on observations and particles released at the location of the Maconda well. The initial conditions (ICs) of oil particle location for the two-dimensional surface oil trajectory forecasts are based on a fusing of all available information including satellite-based analyses. The resulting oil map is digitized into a shape file within which a polygon filling software generates longitude and latitude with variable particle density depending on the amount of oil present in the observations for the IC. The more complex system assumes three (light, medium, heavy) states for the oil, each state has a different removal rate in the Monte Carlo process, three-dimensional particle motion, and a particle size-dependent oil mixing model.Simulations from the two-dimensional forecast system produced results that qualitatively agreed with the uncertain “truth” fields. These simulations validated the use of our Monte Carlo scheme for representing oil removal by evaporation and other weathering processes. Eulerian velocity fields for predicting particle motion from data-assimilative models produced better particle trajectory distributions than a free running model with no data assimilation. Monte Carlo simulations of the three-dimensional oil particle trajectory, whose ensembles were generated by perturbing the size of the oil particles and the fraction in a given size range that are released at depth, the two largest unknowns in this problem. 36 realizations of the model were run with only subsurface oil releases. An average of these results yields that after three months, about 25% of the oil remains in the water column and that most of the oil is below 800m

A Comparison of Sampling Methods for Larvae of Medium and Large Epipelagic Fish Species during Spring Seamap Ichthyoplankton Surveys in the Gulf of Mexico

Annual ichthyoplankton surveys have been conducted in the Gulf of Mexico during spring since 1982 by the National Oceanic and Atmospheric Administration (NOAA) National Marine Fisheries Service (NMFS) Southeast Area Monitoring and Assessment Program (SEAMAP). Historically, ichthyoplankton has been assessed using bongo and surface neuston nets. A new sampling gear, the S-10 net, was tested between 2009 and 2011. This is a 1 × 2 m frame fitted with a 0.505 mm mesh net, towed in a yo-yo fashion between the surface and 10 m. Sampling effectiveness of the three gears was compared by examining the abundance and length of larvae of bluefin tuna (Thunnus thynnus) and seven co-occurring pelagic taxa (Auxis spp., Euthynnus alleteratus, Coryphaena spp., Katsuwonus pelamis, other Thunnus spp., family Istiophoridae, and Xiphias gladius) and vertical distributions of scombrid taxa were examined using MOCNESS samples. Permutational multivariate analysis of variance (PERMANOVA) of net type and time of sampling (day/night) indicated that net type was a significant factor in assessing abundance and length for all taxa. Highest abundances for seven of eight taxa were in S-10 samples, and MOCNESS samples confirm highest scombrid abundance between the surface and 20 m. Our results show sampling effectiveness strongly depends on the depth fished by the net and that the S-10 net was more effective than standard SEAMAP bongo and neuston nets. Thus, future sampling with the S-10 net may improve the annual index of larval abundance for the western population of Atlantic bluefin tuna, traditionally based on abundance from bongo samples

Satellite Remote Sensing of Surface Oceanic Fronts in Coastal Waters off West–Central Florida

Two algorithms designed to detect deepwater oceanic features and arbitrary edge profiles were tuned to automatically delineate fronts in coastal waters off west–central Florida using satellite-derived sea surface temperature (SST), chlorophyll-a concentration (Chl), normalized water-leaving radiance (nLw), and fluorescence line height (FLH) images during select periods in the spring and fall of 2004 and 2005. The dates correspond to recreational king mackerel, Scomberomorus cavalla, tournaments. A histogram-based algorithm was useful to detect coastal surface SST, nLw, and FLH fronts, specifically. A gradient-based algorithm, with a smaller kernel box of 3 × 3 pixels, best identified nearshore ( \u3c 10 m depth) features in Chl images at the mouth of Tampa Bay, but was less effective for fronts farther offshore where gradients were weaker. Local winds and tide levels estimated from a coastal observing buoy, and bathymetric gradients were examined to help understand the factors that influenced front formation and stability. Periods of strong and variable winds led to front movement of up to 10 km per day or dissipation within 2–3 days in over 80% of the fronts detected in SST, Chl, nLw, and FLH imagery. Short episodes of less variable wind velocities typically led to more stable and stationary fronts, within 3–5 km, for up to four days. The occurrence of fronts closely associated with the coastal bathymetry, namely at the 20 m and 30 m isobaths, was significantly higher in the fall SST imagery and in the spring Chl imagery. Fall SST fronts related to bathymetric gradients likely resulted from progressive cooling of the water with depth. Stronger Chl and nLw443 gradients at the mouths of estuaries in the fall compared to the spring were attributed to increased precipitation and periods of stronger winds or tides. The FLH imagery was most useful in delineating coastal algal blooms. The automatic front detection techniques applied here can be an important tool for resource managers to track coastal oceanographic features daily, over synoptic spatial scales

Satellite Remote Sensing in Support of an Integrated Ocean Observing System

Earth observing satellites represent some of the most valued components of the international Global Ocean Observing System (GOOS) and of the Global Climate Observing System (GCOS), both part of the Global Earth Observation System of Systems (GEOSS). In the United States, such satellites are a cornerstone of the Integrated Ocean Observing System (IOOS), required to carry out advanced coastal and ocean research, and to implement and sustain sensible resource management policies based on science. Satellite imagery and satellite-derived data are required for mapping vital coastal and marine resources, improving maritime domain awareness, and to better understand the complexities of land, ocean, atmosphere, ice, biological, and social interactions. These data are critical to the strategic planning of in situ observing components and are critical to improving forecasting and numerical modeling. Specifically, there are several stakeholder communities that require periodic, frequent, and sustained synoptic observations. Of particular importance are indicators of ecosystem structure (habitat and species inventories), ecosystem states (health and change) and observations about physical and biogeochemical variables to support the operational and research communities, and industry sectors including mining, fisheries, and transportation. IOOS requires a strategy to coordinate the human capacity, and fund, advance, and maintain the infrastructure that provides improved remote sensing observations and support for the nation and the globe. A partnership between the private, government, and education sectors will enhance remote sensing support and product development for critical coastal and deep-water regions based on infrared, ocean color, and microwave satellite sensors. These partnerships need to include international research, government, and industry sectors in order to facilitate open data access, understanding of calibration and algorithm strategies, and fill gaps in coverage. Such partnerships will define the types of observations required to sustain vibrant coastal economies and to improve the health of our marine and coastal ecosystems. They are required to plan, fund, launch and operate the types of satellite sensors needed in the very near future to maintain continuity of observations

Projections of Future Habitat Use by Atlantic Bluefin Tuna: Mechanistic Vs. Correlative Distribution Models

Climate change is likely to drive complex shifts in the distribution and ecology of marine species. Projections of future changes may vary, however, depending on the biological impact model used. In this study, we compared a correlative species distribution model and a simple mechanistic oxygen balance model for Atlantic bluefin tuna (Thunnus thynnus: ABFT) in the North Atlantic Ocean. Both models gave similar results for the recent historical time period, and suggested that ABFT generally occupy favourable metabolic habitats. Projections from an earth system model showed largely temperature-induced reductions in ABFT habitat in the tropical and sub-Tropical Atlantic by 2100. However, the oxygen balance model showed more optimistic results in parts of the subpolar North Atlantic. This was partially due to an inherent ability to extrapolate beyond conditions currently encountered by pelagic longline fishing fleets. Projections included considerable uncertainty due to the simplicity of the biological models, and the coarse spatiotemporal resolution of the analyses. Despite these limitations, our results suggest that climate change is likely to increase metabolic stress on ABFT in sub-Tropical habitats, but may improve habitat suitability in subpolar habitats, with implications for spawning and migratory behaviours, and availability to fishing fleets

Potential Impact of Climate Change on the Intra-Americas Sea: Part 2. Implications for Atlantic Bluefin Tuna and Skipjack Tuna Adult and Larval Habitats

Increasing water temperatures due to climate change will likely have significant impacts on distributions and life histories of Atlantic tunas. In this study, we combined predictive habitat models with a downscaled climate model to examine potential impacts on adults and larvae of Atlantic bluefin tuna (. Thunnus thynnus) and skipjack tuna (. Katsuwonus pelamis) in the Intra-Americas Sea (IAS). An additional downscaled model covering the 20th century was used to compare habitat fluctuations from natural variability to predicted future changes under two climate change scenarios: Representative Concentration Pathway (RCP) 4.5 (medium-low) and RCP 8.5 (high). Results showed marked temperature-induced habitat losses for both adult and larval bluefin tuna on their northern Gulf of Mexico spawning grounds. In contrast, habitat suitability for skipjack tuna increased as temperatures warmed. Model error was highest for the two skipjack tuna models, particularly at higher temperatures. This work suggests that influences of climate change on highly migratory Atlantic tuna species are likely to be substantial, but strongly species-specific. While impacts on fish populations remain uncertain, these changes in habitat suitability will likely alter the spatial and temporal availability of species to fishing fleets, and challenge equilibrium assumptions of environmental stability, upon which fisheries management benchmarks are based