means. The initial 
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ed images were 
and dendrograms 
ies in the relevant 
ographic systems, 
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process is the 
ons. Maintaining 
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bok several steps 
ivironment after 
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larger bordering 
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smaller than 0.5 
ated. Features up 
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SST Spatial 
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CSAT Seasonal 
SST Spatial 
SST Seasonal 
kx 
Figure Four: Australian West Coast Boundaries 
The process used to assess the accuracy of the classification 
in this study is as follows: the basic regional mean (ux) and 
standard deviations (sx) were calculated using the 
individual components used to derive regional boundaries. 
The standard deviation image gave a regional indication of 
areas that contained a large degree of variability. In order to 
gain a clearer pixel-based image of each pixel's association 
with its regional deviation, a binning operation was 
performed producing three bins classifying pixels into their 
relevant deviation (1, 2 and 3 or more) from the regional 
mean (e.g. Figure 5). This provided a discreetly contrasted 
view of pixels that were further away from the regional 
mean character. Some pixels highlighted areas where the 
region boundary had been compromised through the ISO 
classification process using two components or had been 
amalgamated during the class reduction process using 
dendrograms. The pixel indicator of deviations from the 
regional mean in this form only indicates distribution 
characteristic within the classified region. Across the whole 
image all pixels that fall into the three or more deviations 
have the same value. However, should one want to reassess 
particular anomalies of the classification, then there is little 
indication of where best to start in order to find some 
degree of significance. To counter these problems two 
approaches were taken to give a pixel level indicator of its 
scale of regional variability over the whole regional scope. 
Essentially the desired result required a high score for a 
pixel in a high deviation bin in a region with a large 
deviation. A pixel close to the region's mean but in a region 
with a high deviation still scored high but ranked less. A 
region of low deviation and pixels close to the mean were 
ranked the lowest. 
      
   
  
Error Magaitadei Viros Hatoituce 
T. 
   
iit 
  
Figure Five: Local example showing A — Classes; B — 
Standardized region pixel deviation magnitude; and C — 
pixel deviation magnitude. 
DISCUSSION 
A comparison of these results with similarly derived 
regionalisation — showed that the spatio-temporal 
representations have the potential to further inform marine 
spatial management regimes. It was also shown that the 
delimitation of natural phenomena is feasible and can have 
seasonal dynamics represented. This provides a potential 
baseline for near real-time boundary assessments and an 
addition to adaptive management techniques. Ultimately the 
results have shown that there is scope for the improved 
incorporation of marine dynamics into marine spatial 
management systems. 
The methodology presented in this study has provided a 
simple framework for the identification and delimitation of 
both spatial averages and spatio-temporal trends in single 
oceanic properties, namely SST and CSAT. As well as 
creating a systematic approach, the componentised nature 
provides scope for added complexity and changes to 
individual components. This approach reduces major 
redundancy issues and need for potentially frequent large 
scale redesign, and is far more suitable for a process which 
is likely to make advances over the near future. Similar 
approaches are being developed in a number of other 
scientific fields such as bioregionalisation that may 
eventually feed into the overall marine cadastre. The 
methodology presented can also utilise other sources of 
satellite data (or any raster data) that can be at any number 
of spatial resolutions. The key requirements for suitable 
results would be a long enough time-series of data and input 
datasets to be of equal resolution and extents. 
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Cheng, Q., Jing, L. & Panahi, A. (2006) Principal 
component analysis with optimum 
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Eklundh, L. & Singh, A. (1993) A comparative analysis of 
standardised and 
unstandardised principle components analysis in remote 
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Journal of Remote Sensing, 14, 1359 - 1370. 
Hirosawa, Y., Marsh, S. E. & Lkliman, D. H. (1996) 
Application of standardized 
principal component analysis to land-cover characterization 
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