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Title: Integration of GIS, Remote Sensing and Statistical


1
Integration of GIS, Remote Sensing and
Statistical Technologies for Marine Fisheries
Management
Jianjun Wang University of Aberdeen
2
Introduction
? Fisheries resources need to be properly managed
for sustainable exploitation of the worlds
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.
3
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.
4
Projects
5
The area covered by the projects
6
A schematic diagram of the system
7
The GIS based on PC ArcView with user-friendly
interface
8
The GIS based on UNIX Arc/Info with user-friendly
interface
9
The front page of a database based on MS Access
10
Spatio-temporal analysis and modelling
11
Visual analysis base on GIS
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17
The distribution of cuttlefish abundance and the
influence of sea surface temperature
18
Statistical tests
19
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
20
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
  • 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
    degree of freedom from 2 to 4,
  • e.g.
  • 1SSTs(SST,2)s(SST,3)s(SST,4)

? The final optimum model is lpue
s(sst, 4) s(sbs, 4) depth
21
Temporal analysis and modelling The temporal
distribution pattern of hake abundance in SW
Atlantic
22
Integration and use of remotely sensed data
Second order oceanic data Define local relative
SST variability (RV) and gradients
23
The relationship between RV and fish abundance
Is it reliable? Lets see
24
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
25
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
26
THANKS
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