A review of methods for analysing spatial and temporal patterns in coastal water quality

Paul Bierman, Megan Lewis, Bertram Ostendorf, Jason Tanner

    Research output: Contribution to journalArticlepeer-review

    132 Citations (Scopus)


    Coastal environments contain some of the marine world's most important ecosystems and represent significant resources for human industry and recreation. Water quality in the coastal environment is extremely important for a number of reasons from the protection of marine organisms and the well being of marine ecosystems to the health of people in the region and the safety of industries such as aquaculture. As a result it is essential that environmental health in coastal environments is monitored. Traditional monitoring methods include assessment of biological indices or direct measurements of water quality, which are based on in situ data collection and hence are of ten spatially or temporally limited. Remote sensing imagery is increasingly used as a rich source of spatial information, providing more detailed coverage then other methods. But the complexity of information in the imagery requires new analysis techniques that allow us to identify the components and possible causes of spatial and temporal variability. This paper presents a review of methods to analyse spatial and temporal variations in remote sensing data of coastal water quality and discusses and compares these methods and the outcomes they achieve. Selected techniques are illustrated by using a sample dataset of MODIS chlorophyll-a imagery. We consider classification methods (cluster analysis, discriminant analysis) that may be used in exploratory, confirmatory and predictive ways, methods that summarize and identify patterns within complex datasets (factor analysis, principal components analysis, self-organizing maps), and techniques that explicitly analyse spatial relationships (the semivariogram and geographically weighted regression). Each technique has a different purpose and addresses different questions. This review identifies how these methods can be utilized to address water quality variability in order to foster a wider application of such techniques for coastal water quality assessment and monitoring.

    Original languageEnglish
    Pages (from-to)103-114
    Number of pages12
    JournalEcological Indicators
    Issue number1
    Publication statusPublished - Jan 2011


    • Cluster analysis
    • Coastal environments
    • Data analysis
    • Discriminant analysis
    • Factor analysis
    • Geographically weighted regression
    • Principal components analysis
    • Self-organizing maps
    • Semivariogram
    • Water quality


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