Marine Science Faculty Publications

Change Detection in Shallow Coral Reef Environments Using Landsat 7 Etm+ Data

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Algae, Atmospheric correction, Carysfort, Kaneohe, Moorea, SeaWiFS

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This paper aims to clarify the potential of the new Landsat 7/Enhanced Thematic Mapper Plus (ETM+) sensors for change detection in coral reef environments. We processed images of two reef sites in Florida and Hawaii acquired over short time intervals (2 weeks and 3 months). During these periods, reefs were not affected by major disturbances (phase shift, strategy shift, bleaching, and hurricanes). This stability allowed us to assess the bias in change detection analysis. Two methods for change detection analysis were applied. The first one estimates the atmospheric conditions (Rayleigh and aerosol radiances, ozone and diffuse transmittances) using an ETM+/SeaWiFS multisensor approach. The second method is an empirical correction based on pseudoinvariant features that compensates for different atmospheric conditions as well as for any sensor (noise) or environmental (water column, sea surface state) conditions. The atmospheric correction alone did not provide an accurate match in images across time due to significant whitecaps and possible sun glint and its products required an empirical adjustment. Therefore, for the images in this study there was not substantial benefit in performing an atmospheric correction compared to an empirical correction alone. Both methods resulted in a minimum uncertainty of 4, 3, and 3 digital counts, respectively, in ETM+ Bands 1-3. Finally, we completed the study of real images by the analysis of ETM+ reflectance spectra for a large variety of coral reef objects. We concluded that the assessment of the rates of change in three ubiquitous classes 'sand,' 'background' (including rubble, pavement, and heavily grazed dead coral structure), and 'foreground' (including living corals and macroalgae) emerges as the most reproducible and feasible application for the ETM+ sensor.

Citation / Publisher Attribution

Remote Sensing of Environment, v. 78, issue 1-2, p. 150-162