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Thermal Applications

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Coral Reef Remote Sensing

Abstract

Of all the remote sensing products, sea surface temperature (SST) is arguably the most widely used data stream, both by scientists and especially by coral reef managers and stakeholders. The technology is mature and considerable effort has gone into creating high-quality time-series, climatologies, and issue-specific products to help managers and lay-people assess the risk of thermal stress in marine ecosystems. The effort that has gone into developing and improving global thermal stress products such as HotSpots, Degree Heating Weeks, and regional products such as ReefTemp, is in large part a reflection of how useful they have become to reef managers, reef users and scientists across a wide range of disciplines. In recent years, further development has seen SST ‘now-casting’ products extended to seasonal forecasting products with lead times of up to 6 months, albeit with reduced accuracy on long time horizons. This has been achieved by incorporating real-time SST data into sophisticated statistical, as well as dynamical, coupled ocean–atmosphere models. Increasing use of real-time and historic SST data in models, in conjunction with other data sources, is helping address more complex ecological and management problems.

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Acknowledgments

The authors would like to thank Peter Minnett, Ken Casey, Helen Beggs, Claire Spillman, Jeff Maynard and William Skirving for helpful comments on components of this chapter. Thanks to Gang Liu and Al Strong for assistance with Fig. 12.1. We also acknowledge and thank Peter Mumby, Scott Woolridge, and Brad Congdon for allowing us to use their work as case studies to solving more complex questions. The manuscript contents are solely the opinions of the authors and do not constitute a statement of policy, decision, or position on behalf of NOAA or the U.S. Government.

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Correspondence to Scarla J. Weeks .

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Weeks, S.J., Berkelmans, R., Heron, S.F. (2013). Thermal Applications. In: Goodman, J., Purkis, S., Phinn, S. (eds) Coral Reef Remote Sensing. Springer, Dordrecht. https://doi.org/10.1007/978-90-481-9292-2_12

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