Integration of GIS, Remote Sensing and Statistical Technologies for

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Integration of GIS, Remote Sensing and Statistical
Technologies for Marine Fisheries Management
Jianjun Wang
University of Aberdeen
Introduction
 Fisheries resources need to be properly managed for sustainable
exploitation of the world’s living aquatic resources .
 It has been realized that the traditional fisheries management, which
considers the target species as independent, self-sustaining
populations, is insufficient
 EAF: Ecosystem Management for Sustainable Marine Fisheries has
been becoming popular.
 However, it has been realized that, a working ecosystem approach
management depends on a boarding of data and information on
environmental, biological and social aspects, analysis and modeling
technologies.
Data
Data
Data
Data
Data
Data
Remote Sensing Technology
Remote sensing has gained increasing importance in studies of marine
systems, for extracting oceanographic information, and monitoring the
dynamics of oceanic environment
GIS Technology
GIS technology has proven to be an indispensable tool for integrating,
managing and visualising spatially distributed data, discovering hidden
patterns that other numerical methods could not find, and providing maps.
Statistical technology
Statistical and geo-statistical analyses and modelling have been widely
used to provide quantitative description and predictions about living
marine resources
However, the success of such approaches has been limited due to the
complex nature of the four-dimensional marine environment and fish
distribution, the complex spatio-temporal relations between them – and
the occurrence of anomalies in distribution and abundance caused by
anomalies in environmental conditions.
Projects:
Cephalopod Resources Dynamics: Patterns in Environmental and Genetic
Variation(CEC FAIR programme, 1997-2000 )
Environmental Influences on the Distribution of Commercial Fish Stocks (NERC
small grant project, 1999)
Data collection for assessment of the main finfish stocks in the Patagonian shelf
(SW Atlantic). (CEC DG Fisheries Study Project, 2000-01)
Department of Trade and Industry Strategic Environmental Assessment: An
Overview of Cephalopods Relevant to the SEA4 area. Geotek Ltd, 2003
Promoting higher added value to a finfish species rejected to sea (ROCKCOD).
(CEC DG Fisheries CRAFT project, 2003-04)
Cephalopod Stocks in European Waters: Review, Analysis, Assessment and
Sustainable Management (CEPHSTOCK). (CEC Framework 5 Concerted Action,
2002-05 )
The area covered by the projects
140°
130°
120°
110°
100°
70°
60°
50°
40°
30°
20°
10°
0°
10°
20°
30°
40°
50°
60°
70°
80°
90°
100°
110°
60°
50°
50°
40°
40°
30°
30°
20°
20°
10°
10°
0°
0°
62°
57°
10°
52°
Haul locations
(Spanish data, 1989 - 1999)
20°
41°
30°
1989
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Depth (m)
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3000m
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50°
60°
67°
62°
20°
41°
Year
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51°
80°
60°
10° 67°
46°
90°
30°
46°
40°
50°
51°
#
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60°
57°
52°
70°
70°
140°
130°
120°
110°
100°
90°
80°
70°
60°
50°
40°
30°
20°
10°
0°
10°
20°
30°
40°
50°
60°
70°
80°
90°
100°
110°
A schematic diagram of the system
The GIS based on PC ArcView with user-friendly interface
The GIS based on UNIX Arc/Info with user-friendly interface
The front page of a database based on MS Access
Spatio-temporal analysis and modelling
Spatial / temporal
Analysis and modelling
Visual analysis
Data explanatory analysis
Correlation
Classification
Refine
Auto-correlation
Spatial Correlograms
Variogram
Statistics
GIS
Modelling
……
Refine
Outputs
Visual analysis base on GIS:
The distribution of cuttlefish abundance and the influence of sea
surface temperature
Cuttlefish abundance (LPUE, kg/hr) density and SST (1990)
9.0
10.0
11.0
10
.0
>0 - 5
11.0
11.0
5 - 10
12.0
10 - 15
12.0
12.0
12.0
LPUE
(k g/hr)
.0
10.0
0
9.
11.0
10.0
10.0
11.0
1990
Warm year
10
.0
10
9.0
9.0
9.0
9.0
10.0
15 - 20
13.0
13.0
13.0
13.0
20 - 25
14.0
25 - 30
Jan
Fe b
Mar
Ap r
30 - 35
13.0
35 - 40
17.
0
12.0
.0
12
40 - 45
45 - 50
14.0
18.0
16.0
50 - 55
15.0
19.0
15.0
16.0
19 .0
19
.0
May
65 - 70
.0
21
20
.0
18.0
.0
17
60 - 65
20.0
18.0
17.0
16.0
55 - 60
17.0
Jun
22
.0
14.0
Jul
70 - 75
Au g
75 - 80
12.0
9.0
16.0
13.0
90 - 95
95 - 100
12.0
16.0
18.0
> 100
14.0
17.0
19.0
DEP TH
13.0
15.0
High fish
abundance
appeared
after warm
hatching
season
20.0
15.5
18.0
.5
15
Oct
Sep
200m
Nov
De c
Cuttlefish abundance (LPUE, kg/hr) density and SST (1991)
9.0
8.0
8.0
LPUE
(k g/hr)
9.0
7.0
9.0
9.0
8.0
9.0
7.0
8.0
10.0
6. 0
>0 - 5
10.
0
9.0
10.0
5 - 10
10.0
11.0
11.0
10 - 15
11.0
15 - 20
11.0
12.0
12 .0
12.0
20 - 25
12.0
25 - 30
Jan
Fe b
Mar
Ap r
16.5
.0
11
16
.0
.0
10
16.0
45 - 50
17.0
50 - 55
55 - 60
16.0
14.0
18.0
60 - 65
1 7.0
15.0
19.0
.0
18
.0
20
19
17
.0
May
65 - 70
2 0.0
.0
1 6.0
.0
13.0
35 - 40
15.0
13.0
12
.0
30 - 35
40 - 45
12
.0
70 - 75
21
11.
0
Jun
Jul
Au g
75 - 80
14.0
12.5
5
1 5.
11.0
80 - 85
85 - 90
.0
10
12.0
11.0
.5
12
16.5
17.0
9. 0
11.5
17.5
90 - 95
95 - 100
18.0
15.0
16.0
20.0
.0
21
Sep
DEP TH
14.0
17 .0
22.0
> 10 0
12.0
13.0
19.0
15.0
1991
Cold year
80 - 85
85 - 90
13.5
15
.5
11.0
11.0
.0
10
.0
13
13 .5
15.5
15.0
17.0
9.0
The centre of
high
abundance
located
further north
in warmer
year than in
cold year
13.0
Oct
13.0
200m
Nov
De c
Very low fish
abundance
appeared
after cold
hatching
season
Statistical tests
Tests to look at relationship
50
0
50
-1.0-0.50.0 0.5 1.0
rho
0
100 150 200 250
January: Distance (n.m.)
rho
100 150 200 250
July: Distance (n.m.)
0
50
100 150 200 250
Aprl: Distance (n.m.)
0
50
100 150 200 250
October: Distance (n.m.)
-1.0-0.50.0 0.5 1.0
rho
rho
Spatial empirical
correlograms (rho) for
long-term average LPUE in
4 months in different
seasons
-1.0-0.50.0 0.5 1.0
Tests to look at spatial
correlation
-1.0-0.50.0 0.5 1.0
The correlation between
cuttlefish abundance and
sea surface temperature
(SST)
Spatial classification
Spatial classification of squid Loligo spp. abundance in
the NE Atlantic Water
 12 monthly long-term averaged LPUE
(landings per unit effort (kg/h) variables
 Principal components analysis (PCA)
was used to reduce the complexity of the
data, and to remove the correlation
Cluster analysis was used to define
areas with similar spatio-temporal
patterns of LPUE, and LPUE level.
 Display and refine the result
SEASONAL CHANGES OF LPUE (Kg/Hr) IN FIVE AREAS
0
1
LPUE (Kg/Hr)
2
3
4
5
Area1
Area2
Area3
Area4
Area5
2
4
6
Time in Month
8
10
12
Spatial modelling
Generalized additive model (GAM)
g(x) =  + f1(x1) + f2(x2) +  fi(xi)
where  is a constant intercept, each of the xi are the predictors and the fi are
functions of the predictors or terms
Modelling Squid abundance in relation with environmental variables in the
Northern North Sea
 The response: LPUE
The initial predictor variables with
Norway
the input terms:
1. sea surface temperature (SST)
2. sea bottome temperature (SBT)
3. sea surface salinity (SSS)
4. sea bottom salinity (SBT)
5. Depth
in the terms of lineal, splines smoother with
4A
degree of freedom from 2 to 4,
e.g.
Loligo spp. (January, 1997)
1+SST+s(SST,2)+s(SST,3)+s(SST,4)
LPUE (kg/h)
Depth (m)
Scotland
Fitted LPUE (kg/h)
200
 The final optimum model is:
lpue ~ s(sst, 4) + s(sbs, 4) + depth
5°
3°
1°
1°
3°
5°
62°
62°
60°
60°
58°
58°
5°
3°
1°
1°
3°
5°
Temporal analysis and modelling – The temporal distribution
pattern of hake abundance in SW Atlantic
Data explanatory analysis:
Train-based model:
Visual analysis:
Long-term monthly average LPUE (Common Hake, Merluccius hubbsi) and SST (1989 - 1999)
SST (C d eg ree)
Long-term monthly average LPUE (Common Hake, Merluccius hubbsi) and SST (1989 - 1999)
0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
5
5.5
6
6.5
7
7.5
8
8.5
9
9.5
10
10.5
11
11.5
12
12.5
13
13.5
14
14.5
15
15.5
16
16.5
17
17.5
18
18.5
19
19.5
20
20.5
21
21.5
22
22.5
23
23.5
24
24.5
25
25.5
26
26.5
27
SST (C d eg ree)
Long-term monthly average LPUE (Common Hake, Merluccius hubbsi) and SST (1989 - 1999)
0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
5
5.5
6
6.5
7
7.5
8
8.5
9
9.5
10
10.5
11
11.5
12
12.5
13
13.5
14
14.5
15
15.5
16
16.5
17
17.5
18
18.5
19
19.5
20
20.5
21
21.5
22
22.5
23
23.5
24
24.5
25
25.5
26
26.5
27
SST (C d eg ree)
Long-term monthly average LPUE (Common Hake, Merluccius hubbsi) and SST (1989 - 1999)
Feb
Jan
Mar
Feb
Jan
Apr
Mar
Feb
Jan
Feb
Jan
May
Apr
Mar
Jun
May
Mar
Aug
Jul
Jun
Apr
Aug
Jul
LP UE (kg/hr)
May
Jun
May
Sep
Jun
Oct
Sep
Nov
Oct
Sep
Sep
Dec
Nov
Oct
0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
5
5.5
6
6.5
7
7.5
8
8.5
9
9.5
10
10.5
11
11.5
12
12.5
13
13.5
14
14.5
15
15.5
16
16.5
17
17.5
18
18.5
19
19.5
20
20.5
21
21.5
22
22.5
23
23.5
24
24.5
25
25.5
26
26.5
27
d)
Dec
Nov
Oct
Aug
Jul
SST (C d eg ree)
0
>0 - 5 0
50 - 10 0
100 - 1 50
150 - 2 00LP UE (kg/hr)
200 - 2 50
0
250 - 3 00
>0 - 5 0
300 - 3 50
50 - 10 0
350 - 4 00
100 - 1 50
400 - 4 50
150 - 2 00LP UE (kg/hr)
450 - 5 00
200 - 2 50
0
500 - 5 50
250 - 3 00
>0 - 5 0
550 - 6 00
300 - 3 50
50 - 10 0
600 - 6 50
350 - 4 00
100 - 1 50
650 - 7 00
400 - 4 50
150 - 2 00LP UE (kg/hr)
700 - 7 50
450 - 5 00
200 - 2 50
750 - 8 00
0
500 - 5 50
250 - 3 00
800 - 8 50
>0 - 5 0
550 - 6 00
300 - 3 50
850 - 9 00
50 - 10 0
600 - 6 50
350 - 4 00
100 - 1 50
900 - 9 50
650 - 7 00
400 - 4 50
950 - 1 000
150 - 2 00
700 - 7 50
450 - 5 00
>10 00
200 - 2 50
750 - 8 00
500 - 5 50
250 - 3 00
800 - 8 50
550 - 6 00
300 - 3 50
850 - 9 00
600 - 6 50
350 - 4 00
900 - 9 50
650 - 7 00
400 - 4 50
950 - 1 000
700 - 7 50
450 - 5 00
>10 00
750 - 8 00
500 - 5 50
800 - 8 50
550 - 6 00
850 - 9 00
600 - 6 50
900 - 9 50
650 - 7 00
950 - 1 000
700 - 7 50
>10 00
750 - 8 00
Aug
Jul
Apr
0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
5
5.5
6
6.5
7
7.5
8
8.5
9
9.5
10
10.5
11
11.5
12
12.5
13
13.5
14
14.5
15
15.5
16
16.5
17
17.5
18
18.5
19
19.5
20
20.5
21
21.5
22
22.5
23
23.5
24
24.5
25
25.5
26
26.5
27
Dec
Nov
Dec
800 - 8 50
850 - 9 00
900 - 9 50
950 - 1 000
>10 00
4
Integration and use of remotely sensed data
The first order oceanic
data:
Ocean colour
SST
……
Surface height
Roughness
Second order oceanic data: Define local relative SST variability (RV) and gradients
66°
44°
Local relative
SST variability
(RV)
65°
60°
55°
50°
45°
64°
62°
60°
T
$
T
$
T
$
T$
$
T
T
$
T
$
T
$
T$
$
T
T
$
T
$
T$
$
T
T
$
46°
40°
48°
T
$
50°
30°
50°
45°
$$
T
T
T
$
T$T
$
T
$
0.1 - 500
500 - 1000
T
$
1000 - 2000
T
$
2000 - 3000
T
$
3000 - 6000
SST
35°
-2 - - 1
12 - 12. 5
12. 5 - 1 3
-0 .5 - 0
13 - 13. 5
0 - 0 .5
13. 5 - 1 4
0.5 - 1
14 - 14. 5
1 - 1 .5
14. 5 - 1 5
1.5 - 2
15 - 15. 5
2 - 2 .5
15. 5 - 1 6
2.5 - 3
16 - 16. 5
3 - 3 .5
16. 5 - 1 7
3.5 - 4
17 - 17. 5
Depth (m )
200
500
54°
SST re lativ e
Var iety
0.1 - 0.2
0.2 - 0.4
0.4 - 0.6
0.6 - 0.8
0.8 - 1
30°
55°
500
17. 5 - 1 8
55°
1000
18 - 18. 5
2000
18. 5 - 1 9
3000
19 - 19. 5
4000
65°
19. 5 - 2 0
20 - 20. 5
60°
55°
50°
45°
35°
20. 5 - 2 1
66°
4.5 - 5
5 - 5 .5
5.5 - 6
40°
6 - 6 .5
T
$
T
$
T
$
T$
$
T
T
$
T
$
50°
52°
T
$
TT$
$
T
T $
$
T
$
T
T$
$
T
$
T$
$
T
T
$
T
$
T
$
T
$
T
$
T$
T$
T
T$
$
T$
T
$
T
$
T$
$
T$
$
T
T
54°
64°
62°
60°
58°
CPUE with background of RV
4 - 4 .5
40°
52°
Depth (m )
200
-1 - - 0.5
50°
40°
Sea surface temperature, day 256 - 261, 1989
SST (c degrees)
T
$
$
T
T
$
T
$
48°
45°
T
$
55°
T
$
T
$
T
$
T
$
T
$
CPUE
60°
46°
T
$
T
$
T
$
T
$
40°
50°
65°
44°
T
$
T
$
Relative SST variety in grid and SST
in contour lines (day 256 - 261, 1989)
45°
70°
58°
M. hubbsi CPUE and SST relative variety
(day 256 - 262, 1989)
6.5 - 7
7 - 7 .5
66°
7.5 - 8
64°
62°
60°
58°
8 - 8 .5
8.5 - 9
44°
9 - 9 .5
9.5 - 10
10 - 10. 5
45°
45°
10. 5 - 1 1
M. hubbsi CPUE and SST gradient
(day 256 - 262, 1989)
44°
11 - 11. 5
T
$
11. 5 - 1 2
T
$
65°
50°
60°
55°
50°
50°
T
$
46°
65°
60°
55°
50°
45°
T
$
T
$
48°
48°
T
$
40°
$$
T
T
45°
T
$
0.1 - 500
500 - 1000
T
$
1000 - 2000
T
$
2000 - 3000
T
$
3000 - 6000
T
$
50°
T
$
T
$
T
$
CPUE
45°
50°
50°
52°
Gradients
T
$
$
T
T
$
T
$
40°
55°
70°
46°
T
$
T
$
T
$
T
$
T
$
T
$
T
$
40°
55°
T
$
T
$
T
$
T$
$
T
T
$
T
$
T
$
T$
$
T
T
$
T
$
T$
$
T
45°
SST gradients in grid and SST in
contour lines (day 256 - 261, 1989)
55°
54°
65°
60°
55°
50°
45°
T
$
50°
Depth (m )
200
500
52°
TT$
$
T
T $
$
T
$
T
T$
$
SST gradient
0 - 50
50 - 100
100 - 200
200 - 300
300 - 400
66°
T
$
T$
$
T
T
$
T
$
55°
T
$
T
$
T
$
T$
$
T
$
T
$
T
$
T
T
$
T
$
T$
T$
T
$$
T
T$
T
$
T
$
T$
$
T$
$
T
T
54°
64°
62°
60°
58°
CPUE with background of
SST gradients
The relationship between
RV and fish abundance
Spearman’s test
Is it reliable? Let’s see…
Tree-based models
South area: April
Middle area:
April
Middle area: July
The model based on GIS:
An example: A cephalopod migration model based on GIS
The optimum path and corridor between spawning ground and the catch location
Chl-a
Criteria
and
weight
Spawning
ground
SST
SBT
Current
Depth
Model based on GRID
Catch
Location
Discussion
1. GIS provides a good tool for integration and management of spatially
distributed data, and for fishery resources management.
2. As field measurement data are limited, remote sensing is the only
solution for getting regionally covered, time-series environmental data.
In marine environment:
First order data: Surface temperature, surface elevation,
roughness, …
Second order data: regional and local oceanic circulation features
3. The combination of GIS and statistical technologies, provides a convenient
and flexible way for data analysis and modelling.
GIS:
Unique visualization functions, grid-based module
Less powerful statistical analysis and modelling
Statistical technology:
Powerful quantitative analysis and modelling functions
Lack of visualisation functions and grid-based module
THANKS
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