Abstract

Analysis of marine and coastal systems is of fundamental importance to environmental scientists, engineers, and managers. Since the 1960s, remote sensing has played an important role in characterizing the marine environment, with particular emphasis on sea surface features, temperature, and salinity; mapping of shorelines, wetlands, and coral reefs; local fisheries and species movements; tracking hurricanes, earthquakes, and coastal flooding; and changes in coastal upwelling and marine productivity. This chapter reviews marine applications of remote sensing worldwide, exploring contemporary satellite systems, research themes, and analytical methods. In the Galapagos Islands of Ecuador, marine remote sensing has been limited to the use of large-scale daily image-gathering systems, such as CZCS, MODIS, SeaWiFS, and AVHRR, due to persistent cloud cover and constrained research budgets. Recent advances in satellite technology and availability, however, offer new opportunities for remote sensing in the Galapagos archipelago and beyond. Moderate-resolution sensors like SPOT and Landsat continue to be relevant for regional-scale evaluations of marine and coastal environments, identifying hotspots or focal areas for the use of more fine-grained imagery like QuickBird, WorldView-2, and aerial photographs. Radar systems like Aquarius and SAR show promise in new lines of oceanographic research, including sea surface salinity and the differentiation of mangrove subspecies. The use of ancillary or in situ data for calibration and validation of remotely-sensed image analysis can overcome the limitations of sensors used in bathymetric applications, while advances in cellular and GPS technology facilitate real-time reporting from citizen scientists for integrated monitoring of environmental and social change.KeywordsGlobal Navigation Satellite SystemSynthetic Aperture RadarDissolve Organic MatterAdvance Very High Resolution RadiometerAdvance Very High Resolution RadiometerThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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