Two factors ultimately led to the final classification 
procedure used in this study. First, there was the 
identification of the sPCA components into spatial 
(component-1) and temporal (components 2 and 3). These 
representations led to a natural division for interpretable 
layers. Secondly, the efficiency of utilising classified layers 
for assessment or management purposes had to be taken 
into account. As a large number of separate layers can be 
unwieldy for a conclusive assessment, all possible effort 
was made to reduce the number of final layers. In this 
respect the combination of relevant SST and CSAT layers 
for a single classification was explored. This method would 
have resulted in a single layer for both the spatial and 
temporal dynamic representations. Although in the spatial 
realm this is a feasible option, the temporal dynamic realm 
proved more problematic. Important areas of high dynamic 
in one dataset were often cancelled by low dynamic values 
in the same area in the second dataset. For example, the 
Leeuwin Current, which is an important feature in the 
distribution of many commercial marine species, has a 
strong dynamic signature in SST components. However, 
due to is oligotrophic nature it is almost absent in CSAT 
dynamics. The combination of these two datasets tends to 
reduce the signature of the.Leeuwin, which could lead to an 
assumption of reduced importance. On this factor it was 
decided to maintain SST and CSAT datasets separately. 
The Iterative Self Organising Data Analysis (ISO) 
clustering technique was used in this study. This method 
iteratively classifies an image, recalculates statistics and 
reclassifies again. Cells are assigned to a specific class 
Each existing regional polygon was given a unique 
identifier and the vector layer was converted back to a 
raster image with the same extent and resolution as the 
original. The final classified rasters were able to have 
statistics extracted per the individual regions, allowing 
distinct spatial regions to be assessed independently or in 
conjunction with surrounding regions. 
In the final result of the classification process, relevant 
oceanographic regions were isolated. The Leeuwin Current 
in the west was separated into regions, reflecting it 
weakening signal as the current moves eastward. The 
northern Bass Straight showed the opposing influences 
from both western and eastern sources. Upwelling regions 
in the Great Australian Bight, and the Bonney Coast leading 
into Encounter Bay, were maintained as well as major eddy 
regions such as those South of the Leeuwin and in the 
central GAB. The classification results for southern 
Australia are shown in Figure 3 and the results for the west 
Australian coast are shown in Figure 4. 
define it is a more important factor. In other words, a 
uniform degree of high variability within a region is not a 
problem, as long as the region's bounds delimit the region 
of variability relatively accurately. 
using minimum spectral distance to class means. The initial 
first pass is based on arbitrary spectral means. The process 
is repeated until little change in classes occurs or, either a 
minimum spectral distance or a maximum number of 
iterations is reached. The raw classified images were 
imported into the ArcGIS environment and dendrograms 
were created. Using the grouping hierarchies in the relevant 
dendrograms and knowledge of key oceanographic systems, 
similar class groupings were assessed on repetitiveness and 
merged. One of the important knowledge-based 
assumptions in the classification process is the 
identification of spatially significant regions. Maintaining 
these often large areas while removing redundant regions 
can be as simple as eliminating regions under a certain area 
threshold. In this case these processes took several steps 
and were conducted in the vector environment after 
converting the classified raster images. Vector features 
were eliminated by being merged into a larger bordering 
feature which shared the longest border. Initially a hard 
threshold required all features with areas smaller than 0.5 
decimal degree squared (dD2) to be eliminated. Features up 
to 0.1dD2 were assessed and despite a few exceptions all 
were eliminated. Notable were relatively small regions 
located in the open ocean regions. As the open ocean is 
subject to a high degree of spatio-temporal influences, some 
of the regions fail to spatially define a significantly 
common or periodic occurrence. Open ocean regions with 
depths greater than 500m were subjected to a larger area 
threshold of 1 dD2, although care was taken not to 
eliminate features attributed to islands and well known 
anomalies. 
   
CSAT Spatial SST Spatial 
  
CSAT Seasonal SST Seasonal 
Figure 3: Southern Australian Boundaries 
The verification of these results proved a problem since 
conventional accuracy assessment techniques did not apply. 
A pixel's relative fit to a region's character is not in itself 
an accurate measure of the suitability of regional 
boundaries. While interpreting error magnitude in the case 
of this project, it should be remembered that a region that 
evenly contains a high degree of internal heterogeneity is 
just as viable as a region that is heterogeneous. The degree 
to which the region bounds 
  
   
   
    
    
    
    
   
    
    
   
    
     
   
   
    
    
   
   
   
   
    
   
   
  
   
   
  
    
  
  
    
  
  
   
    
    
    
  
      
  
   
   
  
    
  
  
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