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e imagery from 
this study. The 
sPCA) was used 
  
    
to identify the mean state of identifiable natural process and 
to quantifying a degree of spatiotemporal (seasonal) 
dichotomy in detected marine processes. Component 
images from the sPCA provided a spatial representation of 
marine phenomena, while sPCA loadings indicated the 
temporal fluxes exhibited in a given component. 
Classification of significant sPCA components was 
conducted to derive regional boundaries. 
DATA PROCESSING 
Standardised principle component analysis (sPCA) has been 
used to spatially quantify the annular mode and inter- 
annular dynamics of ocean surface processes. This was 
conducted on the four-year monthly binned dataset of 
MODIS Aqua SST and CSAT. In order to validate SPCA 
results a knowledge-based examination was conducted and 
key oceanographic processes were identified. Results of this 
examination are important to assess the ability of the 
analysis to maintain important oceanographic information 
as well as aid the eventual classification of oceanic regions 
for further marine zonation research. 
Principle component analysis is a well established statistical 
technique used to condense multivariate information into a 
set of typical values. Within the realm of remote sensing it 
has often been used for data compression, image 
enhancement, change detection and the examination of 
temporal dimensionality (Hirosawa et al. 1996, Cheng et al. 
2006, Piwowar and Millward 2002, Vlahakis and Pollatou 
1993). For multi-temporal studies, it was suggested by 
Singh and Harrison (1985) that an sPCA yields improved 
image enhancement over PCA (otherwise known as 
unstandardised PCA) especially within the scope of spatio- 
temporal analyses. Standardised principle component 
analysis differs from the normal PCA with the use of a 
correlation matrix instead of a covariance matrix. This 
essentially standardises the original dataset to a mean of 0 
and deviation of 1. Individual standardisation of the original 
data and the use of a covariance PCA would lead to the 
same result as using unaltered data in a correlation PCA or 
sPCA. 
Standardisation produces an equal weighting of all the input 
images and prevents certain features from dominating the 
analysis due to large numerical values (Eklundh and Singh 
1993). The need to cater for the influence of variance 
difference in time-series satellite imagery is important in 
reducing error factors inherent in image acquisition such as 
detector calibration, sun angle and atmospheric 
transmission (Singh and Harrison 1985). Standardisation 
also reduces the influence of large scale singular anomalies 
on higher components that aim to represent an overall 
spatio-temporal To maintain relevance to marine cadastral 
and boundary research it is necessary to produce vector 
regional bounds for natural features identified in the sPCA 
results. The sPCA components in this study were 
regionalised to produced simplified regionalised layers 
CLASSIFICATION AND VERIFICATION 
The results of the sPCA produced rasters of continuous 
coverage. In themselves they can provide a useful baseline 
layer to inform the spatial character and degree of seasonal 
dynamics. However, in terms of management and 
ultimately cadastral needs there is a preference for some 
  
representing the key detected oceanic features. The initial 
classification was conducted with an unsupervised 
clustering algorithm, while the final simplification of 
regions was based on the use of dendrograms and 
knowledge of important features. Spatial components were 
processed separately, while relative spatiotemporal 
components were combined into single spatiotemporal 
layers. The spatial components produced interesting results, 
with a high degree of regional partitioning in coastal 
regions. Spatiotemporal components produced much more 
complex regionalisation, although many important seasonal 
features were easily identified and regionalised. 
As CSAT images were used without alteration, subsequent 
principle components can be interpreted in conjunction with 
loading signatures to indicate relative level of pixel CSAT 
concentration. Although producing and using the SST 
residuals enhances clarification of possible regions of 
interest, the underlying values become non interpretive to 
physical SST values (degrees Celsius). Colour contrast in 
SST residual components (Figure 2) is only indicative of 
the degree of difference from the maxima. Interpretation of 
SST residual components is possible with the help of the 
component loadings. However, the bulk of relevant features 
are identified through knowledge-based examination. For 
an indication of the relevance to real SST, the original 
images were correlated against the components. Although 
subsequent coefficients are much lower than the loadings 
with input residual images, it is still possible to see how 
representative components are of particular seasons. 
  
  
Figure 2: Southern Australian SST sPCA components: (A) 
Component 1 — Spatial Mean; (B) Component 2 — 
summer/winter dichotomy; (C) Component 3 — 
autumn/spring dichotomy 
degree of delineation of features in the sPCA results. As the 
result shows regional similarities, a regional classification 
approach to defining boundaries was deemed the most 
appropriate for this study. Defining particular regional 
boundaries would then provide both a visual reference and 
an analysis tool. The potential vector dataset that can be 
produced from classification results could then be 
incorporated into vector dominated cadastral systems.