Applications of Satellite Data
Jeffrey Polovina (NOAA/PIFSC) and Melanie Abecassis (JIMAR/PIFSC), with slides from Cara Wilson (NOAA/SWFSC)
- Ocean ‘fronts’
- boundaries
- ‘edges’
- River plumes
- Mesoscale circulation patterns: eddies, meanders, ‘loops’
- Convergence zones
- Subsurface thermal structure: MLD, thermocline
- Ocean surface winds
- Ocean currents
- Wave heights
All of these ocean features can be measured, detected or inferred by satellite data.
- Upwelling
- Harmful Algae Blooms (HABs)
- Oil Spills
- Seasonal Transitions
- El Niño events
- Regime Shifts (i.e. PDO)
- Global Climate Change
Climate change can affect the timing and/or intensity of many of the processes.
Climate Data Records (CDRs) of satellite measurements need to be maintained.
- Near-real time to support fishing operations/research cruises
- Understand the link between oceanography and marine resources habitats
- Input to habitat models
- Define and monitor the spatial distribution of biomes
- As input or validation to ecosystem/earth system models
- Identify ecosystem carrying capacity
- Contribute to a habitat model for species distribution and abundance
- Put animal movement in an oceanographic context
- Monitor and predict ecosystem responses from multiple stressors including climate change
- Inform configurations of protected areas
- To describe ocean features that define the foraging habitats of loggerhead sea turtles, albacore and bluefin tunas, and Hawaiian monk seal.
- In fisheries bycatch studies with sea turtles and sea birds
- In habitat models for loggerhead sea turtles, and pelagic fishes
- To develop ecological indicators (coral reef bleaching, biome dynamics, phytoplankton size)
This "lecture" will showcase a number of examples of studies in the Pacific Ocean that used satellite data to characterize species habitat or oceanic features that drive animal movements.
- Overlays and histograms with tagging and fisheries data to describe ocean features and habitats animals use.
- Descriptive oceanography to understand the oceanographic basis of foraging habitats.
From Polovina et al, 2000. Turtles on the edge: Movement of loggerhead turtles (Caretta caretta) along oceanic fronts, spanning longline fishing grounds in the central North Pacific, 1997-1998.
Fisheries Oceanography 9(1):71 - 82
Wind Stress Curl, Surface Chlorophyll a, and 18ºC SST for 180-160ºW
Foley, pers. Comm.
From 2004, larger hooks and different bait greatly reduced turtle interactions in the swordfish fishery. Additionally, based on loggerhead’s use of the TZCF, Dr. Evan Howell developed TurtleWatch to advise fishers of the zone of highest probability of interaction.
Baker et al, 2007
Surface chlorophyll in: March 2000 (top, middle) and March 2004 (bottom) provide an example of interannual variation in northward extent of oligotrophic waters.
Survival of pups at northern Hawaiian atolls is linked to the location of the 18o SST isotherm, a proxy for the TZCF, 1985-2003.
When the front is too far north, productive waters don’t reach the atolls and pup survival is lower.
From Polovina et al 2006.
Meanders in the KEBR from AVISO altimetry
Primary productivity in the KEBR
Loggerhead turtle track: use of eddies in the KEBR
Frequency distributions for loggerhead turtles released from Japan
Overlay of chlorophyll-a concentration and altimetry, shows that when chl-a concentration is high in the KEBR, turtles are there. When chl-a concentration goes down, because of summer stratication, the turtles migrate north, tracking the TZCF. Bringing two sensors together can be very helpful to understand the context of animal migration.
Blue line: max currents velocity from altimetry (m/s) in the longitude range of KEBR.
From Brainard et al, 2018.
- Use satellite data in GLMs and GAMs to build bycatch or habitat models.
- Construct and compare frequency distributions to map habitat
- Map biomes
- Estimate fishery catch or ecosystem carrying capacity.
Wren et al, 2019
Kobayashi et al 2008
Knowledge of BOTH is critical towards characterizing habitat preference.
Habitat selectivity:
- From Strauss (1979), calculated as: LI = utilization – availability
- LI for each of:
- SST
- Chlorophyll-a
- Magnetic total force, declination & inclination
- Use positive values of LI to delineate habitat
- Sum individual indices to produce overall habitat index.
Model output:
- Seasonal climatological habitat indices, for example.
- Habitat maps can be produced for any time interval, and forecast for short-term or long-term.
- Habitat predictions based on climate change, El Niño, La Niña patterns.
from Hazen et al 2013.
Based on 4300 electronic tags, satellite SST and Chl-a, habitat models, and GFDL Earth Systems Model projections.
Losers and winners
Hazen et al 2017
- Use satellite data to model blue whales & ship strike risk in near real time
- 104 OSU Blue Whale tracks
- NASA funded
Hazen et al, 2018.
Using remotely-sensed products, EcoCast maps are produced in near-real time for use by managers and fishers and aim to minimize fisheries bycatch and maximize fisheries target catch. This is a NASA-funded project.
Gove et al, 2016
The island-mass effect increases nearshore phytoplankton biomass by up to 86% over oceanic conditions, providing basal energetic resources to higher trophic levels that support subsistence-based human populations.
Williams et al 2015
Adjusting for other factors, the highest levels of oceanic productivity were associated with more than double the biomass of reef fishes compared to islands with lowest oceanic productivity.
Heenan et al 2016
Reefs in warmer waters have lower browser biomass and greater detritivore biomass. Areas with intermediate wave energy and high chlorophyll a have increased grazer biomass.
- Biome Dynamics
- Spring Bloom Statistics (date, duration, max biomass, total biomass)
- Phytoplankton functional groups and/or size structure
- Primary production
- Coral bleaching
Chassot et al 2011
(a) Map of Longhurst (2007) BGCP
(b) Map of the dynamic BGCP for 2005
Dynamic BGCP were derived from:
- SST based on the AVHRR series
- SeaWiFS Chl a
- salinity (World Ocean database)
- bathymetry (GEBCO)
from Polovina et al 2008
SeawiFS surface chlorophyll climatology with oligotrophic gyres in black.
Changes in the size of the oligotrophic waters between 1998-1999 and 2005-2006:
- in December: North Pacific, North Atlantic
- August: South Pacific
https://coralreefwatch.noaa.gov/satellite/index.php
This map shows the maximum stress level experienced during the most recent seven (7) consecutive days.
from Heron et al 2016
Degree Heating Week (DHW) accumulates when the SST value exceeds the maximum (blue dashed) of the monthly mean climatology values (blue plus) by at least 1 °C (blue solid: Bleaching Threshold).
DHW thresholds of 4 and 8 °C-weeks (red dashed) have been associated with significant coral bleaching, and widespread bleaching and significant mortality, respectively.
Chassot et al. 2011. Satellite remote sensing for an ecosystem approach to fisheries management. ICES68(4)
Rose et al. 2014. Ten ways remote sensing can contribute to conservation. Conservation Biology
Polovina et al, 2000. Turtles on the edge: Movement of loggerhead turtles (Caretta caretta) along oceanic fronts, spanning longline fishing grounds in the central North Pacific, 1997-1998.
Fisheries Oceanography 9(1):71 - 82
Polovina et al, 2001. The transition zone chlorophyll front, a dynamic global feature defining migration and forage habitat for marine resources. Progress in Oceanography, Volume 49, Issues 1–4
Polovina et al 2006. The Kuroshio Extension Bifurcation Region: A pelagic hotspot for juvenile loggerhead sea turtles. Deep Sea Research Part II: Topical Studies in Oceanography. Volume 53, Issues 3–4
Polovina et al 2008. Ocean's least productive waters are expanding. Geophysical Research Letters. vol 35. issue 3.
Polovina et al, 2017. The Transition Zone Chlorophyll Front updated: Advances from a decade of research. Progress in Oceanography. Volume 150
Juranek et al 2012. Biological production in the NE Pacific and its influence on air‐sea CO2 flux: Evidence from dissolved oxygen isotopes and O2/Ar. https://doi.org/10.1029/2011JC007450
Baker et al 2007. Effect of variable oceanic productivity on the survival of an upper trophic predator, the Hawaiian monk seal Monachus schauinslandi. MEPS 346:277-283
Brainard et al 2018. ECOLOGICAL IMPACTS OF THE 2015/16 EL NIÑO IN THE CENTRAL EQUATORIAL PACIFIC. Bulletin of the American Meteorological SocietyVol. 99, No. 1
Wren et al 2019. Variations in black-footed albatross sightings in a North Pacific transitional area due to changes in fleet dynamics and oceanography 2006–2017. Deep Sea Research Part II: Topical Studies in Oceanography. Volumes 169–170
Kobayashi et al 2008. Pelagic habitat characterization of loggerhead sea turtles, Caretta caretta, in the North Pacific Ocean (1997–2006): Insights from satellite tag tracking and remotely sensed data. Journal of Experimental Marine Biology and Ecology. Volume 356, Issues 1–2
Hazen et al 2013. Predicted habitat shifts of Pacific top predators in a changing climate. Nature Climate Change volume 3, p. 234–238
Hazen et al 2017. WhaleWatch: a dynamic management tool for predicting blue whale density in the California Current. Journal of Applied Ecology. vol 54. issue 5.
Hazen et al 2018. A dynamic ocean management tool to reduce bycatch and support sustainable fisheries. Science Advances 30 May 2018: Vol. 4, no. 5.
Gove et al 2016. Near-island biological hotspots in barren ocean basins. Nature Communications volume 7, Article number: 10581
Williams et al 2015. Human, Oceanographic and Habitat Drivers of Central and Western Pacific Coral Reef Fish Assemblages. PLoSONE 10(4): e0120516. doi:10.1371/journal.pone.012051
Heenan et al 2016. Natural bounds on herbivorous coral reef fishes.
Heron et al 2016. Validation of Reef-Scale Thermal Stress Satellite Products for Coral Bleaching Monitoring. Remote Sens. 2016, 8(1), 59
Last modified 3yr ago