Incorporating Sea-surface Temperature to the light-based geolocation model, TrackIt
60th Annual International Tuna Conference
Chi Lam (Tim), Anders Nielsen and John Sibert
http://www.soest.hawaii.edu/PFRP/Kiefer Lab
http://netviewer.usc.edu/
Objectives of the talk
1. Highlight the features of various “kf” Kalman filter geolocation models
2. Show how TrackIt works with sea-surface temperature matching
3. Help appreciate the details and practical usage of the “kf” models
the wine tasting experience to geolocation
A common 2-step approach in geolocation
• Get raw daily positions from tag manufacturer processing
• Reconstruct most probable positions from the raw estimates
• A disconnected one-way process – no feedback and limited use of the original time series
• Extreme outliers are still problematic
Manufacturer Software
kftrack
kfsst
ukfsst
Others
e.g. tidal methods, Easy-Fish Tracker
Goals of the “kf” models
To give us• a track of geographic positions • some ideas about the uncertainities• some quantitative movement parameters
The “kf” familySimilarities
• Underlying movement model– random walk with drift and diffusion
• Observation model– predicts and describes observation error at any given position
• Kalman filter (extended (EKF) or unscented (UKF) )
• Maximum likelihood estimated model parameters
• Most probable track– Weighted average of what is learned from the current position’s data
and the entire track
Differences
Quick recap of the TrackIt model
u, v – velocity in North-South, East-West direction
D – diffusion
Nielsen & Sibert 2007. Can J Fish Aquat Sci 64
Model 2/3
Model 3/3
Raw geolocations vs. TrackIt
Differentscale
Adding sea-surface temperature
Data scenarios
Sea-surface temperature (SST) imagery
1. Reynolds (NOAA old OI set) 1 deg, Weekly
2. NOAA OIsst version 1 (AVHRR only) 0.25 deg, Daily
3. Blended (D. Foley, Coastwatch) 0.1 deg, 5-day
4. MODIS Aqua (NASA GSFC) 0.05 deg, 8-day
3 Mako sharks (SPOT+PAT)From S. Kohin and D. Holts
(SWFSC)
Drifter (with PAT attached)From M. Musyl
(U Hawaii/ NOAA)
Light only vs.
HighRes
Low
Usage tips
1. Always look at the confidence interval (ci)
2. SST may help, but not all the time3. SST works best when there is a sharp gradient4. Imagery resolution doesn’t matter too much5. Pay attention to the smoothing radius (r)6. Overall, SST reduces the width of the confidence region
7. Convergence for a fit is needed!
Reduce the• No. of parameters estimated
(e.g. bsst.ph = -1)• Resolution of SST imagery
(e.g. Reynolds, fixed radius)
Use• Better initial values
(e.g. D.init = 300)
… a balancing act
Just give me the conclusions…
Wine tasting guide to TrackIt
What you will see A. Light only
240 242 244 246 248
24
26
28
30
32
34
B. Reynolds SST
240 242 244 246 248
24
26
28
30
32
34 JulAugSepOctNovDecSPOT
C. NOAA OIsst
240 242 244 246 248
24
26
28
30
32
34
D. Coastwatch Blended
240 242 244 246 248
24
26
28
30
32
34
Longitude (E)
240
242
244
246
248
Jul 2003 Aug 2003 Sep 2003 Oct 2003 Nov 2003 Dec 2003 Jan 2004
Light onlyRey noldsOIsstBlended
SPOTLight CISST CI
Latitude (N)
22
26
30
34
Jul 2003 Aug 2003 Sep 2003 Oct 2003 Nov 2003 Dec 2003 Jan 2004
16
18
20
22
24
SST (C)
Jul 2003 Aug 2003 Sep 2003 Oct 2003 Nov 2003 Dec 2003 Jan 2004
Rey noldsOIsst
BlendedTag SST
Latitude
( N)
Longitude ( E) Date
1. Swirl – General sense; Track segments are colored by month
3. Taste – Understand SST, see how SST gradient matters
2. Sip – Snip out segments, look at confidence interval (shaded)
+ “True” positions (SPOT/GPS)
Yellow line
A. Light only
240 242 244 246 248
24
26
28
30
32
34
B. Reynolds SST
240 242 244 246 248
24
26
28
30
32
34 JulAugSepOctNovDecSPOT
C. NOAA OIsst
240 242 244 246 248
24
26
28
30
32
34
D. Coastwatch Blended
240 242 244 246 248
24
26
28
30
32
34
Longitude (E)
240
242
244
246
248
Jul 2003 Aug 2003 Sep 2003 Oct 2003 Nov 2003 Dec 2003 Jan 2004
Light onlyRey noldsOIsstBlended
SPOTLight CISST CI
Latitude (N)
22
26
30
34
Jul 2003 Aug 2003 Sep 2003 Oct 2003 Nov 2003 Dec 2003 Jan 2004
16
18
20
22
24
SST (C)
Jul 2003 Aug 2003 Sep 2003 Oct 2003 Nov 2003 Dec 2003 Jan 2004
Rey noldsOIsst
BlendedTag SST
Latitude
( N)
Longitude ( E) Date
Mako 1901
C.
A.
D.
B.
A. Light only
240 242 244 246 248
26
28
30
32
34
36
B. Reynolds SST
240 242 244 246 248
26
28
30
32
34
36JulAugSepOctNovDecSPOT
C. NOAA OIsst
240 242 244 246 248
26
28
30
32
34
36
D. Coastwatch Blended
240 242 244 246 248
26
28
30
32
34
36
Longitude (E)
240
242
244
246
248
Jul 2003 Aug 2003 Sep 2003 Oct 2003 Nov 2003 Dec 2003 Jan 2004
Light onlyRey noldsOIsstBlended
SPOTLight CISST CI
Latitude (N)
26
30
34
38
Jul 2003 Aug 2003 Sep 2003 Oct 2003 Nov 2003 Dec 2003 Jan 2004
16
18
20
22
24
26
SST (C)
Jul 2003 Aug 2003 Sep 2003 Oct 2003 Nov 2003 Dec 2003 Jan 2004
Rey noldsOIsstBlendedTag SST
Latitude
( N)
Longitude ( E) Date
Mako 39322
C.
A.
D.
B.
A. Light only
238 242 246 250
18
20
22
24
26
28
30
32
34
JulAugSepOctNovDecJanFebSPOT
B. Reynolds SST
238 242 246 250
18
20
22
24
26
28
30
32
34
C. NOAA OIsst
238 242 246 250
18
20
22
24
26
28
30
32
34
D. Coastwatch Blended
238 242 246 250
18
20
22
24
26
28
30
32
34
Longitude (E)
240
242
244
246
248
Aug 2003 Oct 2003 Dec 2003 Feb 2004
Light onlyRey noldsOIsst
BlendedSPOTLight CI
SST CI
Latitude (N)
18
22
26
30
34
Jul 2003 Sep 2003 Nov 2003 Jan 2004 Mar 2004
14
16
18
20
22
24
SST (C)
Jul 2003 Sep 2003 Nov 2003 Jan 2004 Mar 2004
Rey noldsOIsstBlendedTag SST
Latitude
( N)
Longitude ( E) Date
Mako 1902
C.
A.
D.
B.
A. Light only
192 196 200 204
12
14
16
18
20
22
24
26
28
30
B. Reynolds SST
192 196 200 204
12
14
16
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20
22
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26
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30
SepOctNovDecJan
FebMarAprMaySPOT
C. MODIS Aqua
196 197 198 199 200 201
18
20
22
24
D. Coastwatch Blended
196 197 198 199 200 201
18
20
22
24
Longitude (E)
194
196
198
200
202
Sep 2002 Nov 2002 Jan 2003 Mar 2003 May 2003
Light onlyRey nolds
ModisBlended
SPOTLight CI
SST CI
Latitude (N)
15
20
25
30
Sep 2002 Nov 2002 Jan 2003 Mar 2003 May 2003
22
24
26
28
30
SST (C)
Sep 2002 Nov 2002 Jan 2003 Mar 2003 May 2003
Rey noldsModisBlendedTag SST
Latitude
( N)
Longitude ( E) Date
Drifter
C.
A.
D.
B.
Download
http://www.soest.hawaii.edu/tag-data/software/
• R (2.8 and below)– NOT 2.9.0 (latest, released April, 2009)
– A basic internal R function has changed for unzipping
• Let us know when– it works!
– it doesn’t…
– You have
• Double-tag data
Chi Lam USC
Acknowledgements
This work was sponsored by the Pelagic Fisheries Research Program, University of Hawaii.
We thank Michael Musyl, Suzanne Kohin, David Holts, Dave Foley, Wildlife Computers, and Lotek Wireless for generously sharing data and ideas.
NASA Earth System Science Fellowship
Cheers!