Frank Muller-Karger, Gerardo Toro-Farmer, Digna Rueda
Institute for Marine Remote SensingUniversity of South Florida
Satellite Observations in Support of
LME Governance: A Case Study for Data Exchange in the Wider
Caribbean LME
Exchange of Experiences on LME- related data and information issues
Buenos Aires, Argentina, June, 2013
Requirement for Dynamic LME Governance
2
Governance requires ‘knowledge’ (understanding of what is happening). Knowledge has to be:Co-derived (joint natural + social science
effort)Inexpensive to local governmentsTimely
LMEs are ‘Large’: They require a synoptic framework of observations
LME’s change continuously: They require time series of observations
Synoptic ocean time series
3
Regional-global context to understand processes, stocks, and diversity within different parts of a dynamic LME
Means to quantitative measure change in LME’s
Place point observations in regional context
Initialize and validate simulations / ecological forecasting
Today’s Tools
4
Atlas – today we can show dynamic aspects of an ecosystem
Climatologies (monthly, annual) as ‘baseline’ to measureshort-term changeLong-term trendsOccurrence and impacts of extreme events
Time series (observations and anomalies)Other dynamic information: individual
historic and current observations, forecasts
Prototype datasets for the Caribbean
5
Regional-scale and local satellite data products
Printed Atlas: Wider Caribbean LME
Digital Atlas examples:Caribbean Marine Atlas (IODE)
http://www.caribbeanmarineatlas.net/NOAA Gulf of Mexico Data Atlas
http://gulfatlas.noaa.gov
Satellite derived synoptic data
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Sea surface temperature (SST)1 km
Ocean color (turbidity, CHL, CDOM) 250 m – 1 km
Wind 25 kmSea Surface Height/currents
~100-300 km Sea Surface Salinity
~300 kmGeomorphological and Habitat:
~2 m > 30 mCoastalCoral Reef Millennium Map
Examples of satellite derived data for the
Wider Caribbean LME
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8
Longitude
La
titu
de
SST (C) bimonthly climatogy: Mar-Apr
-85 -80 -75 -70 -65 -60 -55 -50 -45
0
5
10
15
20
25
20
22
24
26
28
30
Mar-Apr
Longitude
La
titu
de
SST (C) bimonthly climatogy: May-Jun
-85 -80 -75 -70 -65 -60 -55 -50 -45
0
5
10
15
20
25
20
22
24
26
28
30
May-Jun
Latit
ude
Longitude
La
titu
de
SST (C) bimonthly climatogy: Sep-Oct
-85 -80 -75 -70 -65 -60 -55 -50 -45
0
5
10
15
20
25
20
22
24
26
28
30
Sep-Oct-85 -80 -75 -70 -65 -60 -55 -50 -45
Longitude
La
titu
de
SST (C) bimonthly climatogy: Nov-Dec
-85 -80 -75 -70 -65 -60 -55 -50 -45
0
5
10
15
20
25
20
22
24
26
28
30
Nov-Dec
-85 -80 -75 -70 -65 -60 -55 -50 -45
LongitudeLongitude
La
titu
de
SST (C) bimonthly climatogy: Jul-Aug
-85 -80 -75 -70 -65 -60 -55 -50 -45
0
5
10
15
20
25
20
22
24
26
28
30
Jul-Aug0
5
10
15
20
25
-85 -80 -75 -70 -65 -60 -55 -50 -45
Longitude
La
titu
de
SST (C) bimonthly climatogy: Jan-Feb
-85 -80 -75 -70 -65 -60 -55 -50 -45
0
5
10
15
20
25
20
22
24
26
28
30
Jan-Feb0
5
10
15
20
25
SST long term bimonthly means (1985-2009)
Lo
ngitu
de
Latitude
SS
T ( C
) bi
mo
nthl
y cl
imat
ogy
: Sep
-Oct
-85
-80
-75
-70
-65
-60
-55
-50
-45
0510152025
20
22
24
26
28
30
30 28 26 24 22 20 (°C)
Application 1: Satellite ClimatologiesExample: Sea Surface Temperature (SST) from AVHRR
9Longitude (°W)
Tim
e (m
onth
)SST (°C)
-75° -73° -71° -69° -67° -65° -63°9°
10°
11°
12°
9°
10°
11°
12°
Latit
ude
(°N
)
Application 1: Satellite ClimatologiesExample: Southern Caribbean upwelling system (coastal SSTs)
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Application 2: Satellite Time SeriesSouthern Caribbean upwelling system
20
21
22
23
24
25
26
27
28
29
30
20
21
22
23
24
25
26
27
28
29
30
-4
-3
-2
-1
0
1
2
3
4
Climatology
Time Series
Anomaly
Weekly time series (March 12-18, 2005)
20
21
22
23
24
25
26
27
28
29
30
°C°C
Anomaly = Time series - Climatology
Application 2: Satellite Time SeriesSouthern Caribbean upwelling system (coastal SST anomalies time series)
Longitude (°W)-75° -73° -71° -69° -67° -65° -63°
9°
10°
11°
12°
9°
10°
11°
12°
Latit
ude
(°N
)°C
Tim
e (
year
)
11
12
Application 2: Satellite Time SeriesSouthern Caribbean upwelling system (coastal SST anomalies time series)
°CT
ime
(ye
ar)
Spanish sardine capture crashed after two
consecutive years of weak upwelling
SS
T B
imon
thly
tren
d (
C 1
0 ye
ars-1
) m
onth
s: M
ar-A
pr
Long
itude
Latitude
-85
-80
-75
-70
-65
-60
-55
-50
-45
0510152025
-1-0.5
00.5
1mas
k
(°C/decade) 1 0.5 0 -0.5 -1
ns
Application 4: Satellite TendenciesBimonthly SST linear trends (1985-2009)
SST Bimonthly trend (C 10 years-1) months: Jan-Feb
Longitude
Latit
ude
-85 -80 -75 -70 -65 -60 -55 -50 -45
0
5
10
15
20
25
-1
-0.5
0
0.5
1mask
Jan-Feb
SST Bimonthly trend (C 10 years-1) months: May-Jun
Longitude
Latit
ude
-85 -80 -75 -70 -65 -60 -55 -50 -45
0
5
10
15
20
25
-1
-0.5
0
0.5
1mask
May-JunSST Bimonthly trend (C 10 years-1) months: Sep-Oct
Longitude
Latit
ude
-85 -80 -75 -70 -65 -60 -55 -50 -45
0
5
10
15
20
25
-1
-0.5
0
0.5
1mask
Sep-Oct
SST Bimonthly trend (C 10 years-1) months: Mar-Apr
Longitude
Latit
ude
-85 -80 -75 -70 -65 -60 -55 -50 -45
0
5
10
15
20
25
-1
-0.5
0
0.5
1mask
Mar-AprSST Bimonthly trend (C 10 years-1) months: Jul-Aug
Longitude
Latit
ude
-85 -80 -75 -70 -65 -60 -55 -50 -45
0
5
10
15
20
25
-1
-0.5
0
0.5
1mask
Jul-Aug
SST Bimonthly trend (C 10 years-1) months: Nov-Dec
LongitudeLa
titud
e
-85 -80 -75 -70 -65 -60 -55 -50 -45
0
5
10
15
20
25
-1
-0.5
0
0.5
1mask
Nov-Dec
Latit
ude
Longitude
13
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What is the relation between Climate Change and Coral
(benthic) health?
Eakin et al. (2010)
Application 5: Thermal Stress and Coral Bleaching
A) Maximum NOAA Coral Reef Watch Degree Heating Week (DHW) during 2005.
(B) means of coral bleached as either percent live coral colonies (circles) or cover (diamonds).
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Can we identify benthic composition?
Is Coral (benthic) coverage changing over time?
• Benthic coverage is affected by natural / anthropogenic events
• Need to monitor / understand interannual variations and ecological shifts
Application 6: Mapping Benthic Coverage
Classified dataset based on Landsat for Looe Key Reef (red: coral, brown: covered hardbottom, yellow: bare hardbottom, green: sand. Palandro et al. (2008)
Decision Support Tools for an Ecosystem
Based ManagementDeveloping a flexible framework for integrated, distributed,
and interlinked regional coastal and marine data atlases based on the NOAA Gulf of Mexico
data atlas
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Goals
Integrate scientific and socio-economic information through an online data atlas to help visualize and analyze historical datasets, understand connectivity, trends, and variabilityin order to help assess the socio-economic implications
Objectives:
• Identify and integrate additional specific data sets
• Implement a framework for embedding regional data atlases
• Enhance the user interface of existing web-based data atlas(es) for displaying, querying and analyzing information, providing meaningful statistics for decision-making
• Develop a prototype for a mobile platform
Decision Support Tools for Ecosystem-Based Management
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Decision Support Tools for Ecosystem-Based Management
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Gulf of Mexico Data Atlas (NOAA)
http://gulfatlas.noaa.gov/
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Decision Support Tools for Ecosystem-Based Management
Currents(m s-1)
CHL long term annual climatology (mg m-3)
Longitude
Latit
ude
-85 -80 -75 -70 -65 -60 -55 -50 -45
0
5
10
15
20
25
0.02
0.05
0.1
0.2
0.5
1
2
5
10
mask
Chlorophyll (mg m-3)
Wind (m s-
1)
Gulf of Mexico Data Atlas (NOAA)
http://gulfatlas.noaa.gov/
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Use existing datasets developed for the Caribbean LME atlas as initial layers for the IODE Caribbean Marine Atlas: (http://www.caribbeanmarineatlas.net/)
Link the Gulf of Mexico and Caribbean Atlases
Develop a framework for an integrated global atlas that:Uses existing (easily available) ocean and land
satellite dataProvides the framework and technology tools to
incorporate new regions around the worldDevelop an inter-operable data platform
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