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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.