Enhanced detection of the coral Acropora cervicornis from satellite imagery using a textural operator

Samuel J. Purkis, Soe Myint, Bernhard M. Riegl

Research output: Contribution to journalArticle

44 Scopus citations

Abstract

The strength of a texture-based classification lies in the fact that it detects spatial patterning as a function of spectral variation within a particular facies class, as opposed to spectral consistency which drives standard probability-driven image classifiers. Following this premise, the Moran's I spatial autocorrelation metric was proven to return values which differed significantly for areas characterised by dense interlocking thickets of the coral Acropora cervicornis versus areas populated by a sparse mixed coral assemblage dominated by Montastrea annularis. The different behaviour of the metric was sufficient to facilitate spatial discrimination of the two assemblages using a supervised classifier with accuracies that surpass the level of prediction offered by standard spectral-based methods. Discrimination was optimum when autocorrelation was evaluated within a moving window with side-lengths ranging between circa. 30-70 m. The discrimination ability is postulated to be linked to intrinsic differences in the spatial-patterning of the two assemblages at scales of tens of metres. The observed patterning can be further related to the growth form and architecture of the differing coral assemblages. The study demonstrates the potential of using kernel-based autocorrelation metrics in unison with satellite data and offers a pertinent tool for monitoring ecologically important coral assemblages that are statistically indistinct using traditional spectral methods.

Original languageEnglish (US)
Pages (from-to)82-94
Number of pages13
JournalRemote Sensing of Environment
Volume101
Issue number1
DOIs
StatePublished - Mar 15 2006

Keywords

  • Acropora cervicornis
  • Coral reef
  • IKONOS
  • Spatial autocorrelation
  • Texture

ASJC Scopus subject areas

  • Soil Science
  • Geology
  • Computers in Earth Sciences

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