Classifying and mapping general coral-reef structure using Ikonos data

Jill Maeder, Sunil Narumalani, Donald C. Rundquist, Richard L. Perk, John Schalles, Kevin Hutchins, Jennifer Keck

Research output: Contribution to journalArticlepeer-review

82 Scopus citations


Monitoring coral reef, seagrass, and sand features using contemporary remotely sensed data may prove to be a cost-effective and time-efficient tool for reef surveys, change detection, and management. Previous attempts at subsurface feature discrimination with satellite remote sensing have been limited in accuracy due to the effects of pixel mixing associated with poor spatial resolutions. While aerial reconnaissance may offer higher spatial resolutions than satellite sensors, it is often limited by the high costs of planning and implementing the missions, image rectification, area that can be covered, and repeat coverage. In this study, the Ikonos satellite with a 4- by 4-m spatial resolution in the multispectral bands was used as a tool for subsurface feature identification. The Single-Image Normalization Using Histogram Adjustment was used for atmospheric corrections on the imagery. Classification was performed using bands 1, 2, and 3 (blue, green, and red) to maximize the water-penetration capabilities of the sensor. An accuracy assessment of the classification results was performed using in situ data collected at 62 points one day prior to the image being acquired. It was concluded that the Ikonos data were useful for discriminating sand, coral reef (at two depth intervals), and seagrass features (providing overall accuracies of 89 percent each for the two study areas). However, error still remained in discriminating small, diverse patch-reef features. This error (producers accuracy 67 percent) was found in the "Reef ≤ 5 m" class and was primarily attributed to the diversity of this spectral class, which may lead to a spectral signature based on the dominant cover type in a given pixel.

Original languageEnglish (US)
Pages (from-to)1297-1305
Number of pages9
JournalPhotogrammetric Engineering and Remote Sensing
Issue number12
StatePublished - Dec 1 2002

All Science Journal Classification (ASJC) codes

  • Computers in Earth Sciences


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